# Media Slant is Contagious111We thank for helpful comments and suggestions
Julia Cagé, Mirko Draca, Ruben Durante, Claudio Ferraz, Roland Hodler, Eunji
Kim, Michael Knaus, Ro’ee Lee, Josh McCrain, Giovanni Prarolo, Pia Raffler,
Jim Snyder, David Yang, Ekaterina Zhuravskaya, and seminar and conference
participants at the Harvard Political Economy Seminar, Econometric Society
World Congress, Bologna Workshop on Elections, Democracy, and Populism,
Bolzano Workshop on Political Economy, Bergamo Advances in Economics Workshop,
University of St.Gallen Economics Seminar, and Zurich Text as Data Workshop.
Romina Jafarian and Matteo Pinna provided excellent research assistance. This
version: February 2022 (First version: December 2020)
Corresponding author: Philine Widmer.
Email addresses<EMAIL_ADDRESS>(Philine Widmer),
<EMAIL_ADDRESS>(Sergio Galletta<EMAIL_ADDRESS>(Elliott Ash).

Philine Widmer Sergio Galletta Elliott Ash University of St.Gallen  ETH Zürich
University of Bergamo

###### Abstract

This paper analyzes the influence of partisan content from national cable TV
news on local reporting in U.S. newspapers. We provide a new machine-learning-
based measure of cable news slant, trained on a corpus of 40K transcribed TV
episodes from Fox News Channel (FNC), CNN, and MSNBC (2005-2008). Applying the
method to a corpus of 24M local newspaper articles, we find that in response
to an exogenous increase in local viewership of FNC relative to CNN/MSNBC,
local newspaper articles become more similar to FNC transcripts (and vice
versa). Consistent with newspapers responding to changes in reader
preferences, we see a shift in the framing of local news coverage rather than
just direct borrowing of cable news content. Further, cable news slant
polarizes local news content: right-leaning newspapers tend to adopt right-
wing FNC language, while left-leaning newspapers tend to become more left-
wing. Media slant is contagious.

JEL codes: L82, C53, D72, D23, O33, Z13.

Keywords: Media slant, text as data, local news markets.

##  Introduction

Media regulation in democracies typically aims at providing consumers with a
wide choice of (and unrestricted access to) independent news sources. So far,
the literature has chiefly addressed whether news sources are independent from
the government or other special interest groups [e.g., Besley and Prat,,
2006]. In comparison, little attention has been dedicated to whether different
news sources are independent from each other. That is, little is known about
whether the (politically biased) news messaging of a given outlet distorts the
output of other media organizations. This question is relevant for media
regulators since diverse news sources only translate into diverse reporting if
they do not strongly respond to each other (i.e., do not report in unison).

This paper seeks to fill this gap. We study the influence of partisan news
messaging by large, national media outlets on smaller, local outlets. Such
“contagion” could occur as the preferences of local consumers shift and the
small outlets respond through changes in content [Mullainathan and Shleifer,,
2005]. Specifically, we ask whether the “media slant” of cable TV channels
spreads to local newspapers in the United States.

The first step in answering this question is to measure the influence of
partisan cable news on local newspaper content. For this purpose, we build a
corpus of 24 million article snippets from 600$+$ U.S. local newspapers in the
United States for the years 2005 through 2008. We combine these texts with
transcripts of 40 thousand episodes from national cable news networks,
specifically Fox News Channel (FNC), CNN, and MSNBC. We use this parallel
corpus to construct a novel measure of cable news slant – that is, we train a
machine learning model to predict, for a given body of text, whether it
resembles the language by the relatively conservative network (FNC), rather
than language by the relatively liberal networks CNN or MSNBC. We validate the
model and associated predictions with human annotations.

After this validation, we apply our model to the corpus of local newspaper
articles. For each article, we then have a text-based metric that indexes
similarity to language from FNC shows, relative to CNN and MSNBC shows. We
aggregate the article similarities at the newspaper-level to get a novel
measure of partisan slant of local newspapers. We add rich metadata on
newspaper circulation, television channel positioning and ratings, as well as
political and demographic covariates.

We then investigate whether relative similarity to language in a cable news
network increases in response to higher viewership in a newspaper’s market.
Cross-sectional estimates of this relationship would likely be confounded, for
example by more ideologically conservative counties having both higher Fox
News viewership and more conservative local news reporting. To obtain causal
estimates, we exploit exogenous variation in cable news exposure across
counties coming from variation in the relative channel numbering of the three
cable networks [Martin and Yurukoglu,, 2017]. We provide a number of checks to
validate first stage relevance and exogeneity of the instrument. In
particular, the instrument (channel position in cable television line-ups) is
uncorrelated with other local characteristics that are predictive of
viewership or are predictive of the relevant dimensions of local newspaper
content.

We find that higher cable news network viewership increases the influence of a
network’s content on local newspaper articles.222Our estimates imply that a
one-standard-deviation increase in a county’s FNC viewership (relative to
averaged CNN and MSNBC viewership) would increase the similarity of the local
newspaper’s content to FNC’s content by 0.31 standard deviations. To give some
more intuition for the magnitudes, our estimate means that decreasing the
relative FNC channel position by 11 positions would shift slant towards FNC by
about 3 percent of the difference between FNC and CNN/MSNBC. Our estimated
local average treatment effects survive a number of specification checks,
including controls for local demographics, local cable television market
characteristics, and text readability metrics (e.g. word length). The results
are robust to alternative design choices in sampling, weighting, and
instrument construction.

Turning to mechanisms, we first investigate whether the shift in slant is due
to coverage of partisan topics or changes in how the same topics are framed.
The evidence suggests that framing effects largely drive the shifts. Even when
controlling for the topic that an article addresses – such as the economy,
education, or crime – we still find that newspapers more exposed to FNC use a
more FNC-like slant, and vice versa for CNN/MSNBC.

Next, we check whether local newspapers weave the FNC- or CNN/MSNBC-like slant
into their original reporting. We devise a topic-based procedure to
distinguish local news from non-local (that is, national or international)
news. We find that cable news influences both local and non-local news. Since
cable TV shows cover national or international stories, we can thus conclude
that the observed diffusion of media slant is not only direct borrowing of
content from the cable TV channels. Cable news exposure shifts the original
local reporting of the newspapers, consistent with a response to shifts in
reader preferences for slanted news.

Finally, we investigate whether cable news has polarized local news content.
We split newspapers into three groups: those who have historically endorsed
Democrats, those who have historically endorsed Republicans, and those without
or with mixed endorsements. We find that exposure to Fox News (relative to
CNN/MSNBC) tends to polarize local reporting: historically Republican
newspapers become more conservative (FNC-like), while historically Democrat
newspapers become more liberal (CNN/MSNBC-like). Thus, media slant from cable
news seems to encourage outlets to re-position themselves on the ideological
spectrum in response to a more partisan consumer base. Cable news has remade
news landscapes and increased political polarization in local news discourse.

These findings add to the literature in political science and political
economy on biased media (e.g., Ashworth and Shotts,, 2010; Prat,, 2018).333For
surveys on the empirical and theoretical literature, see Puglisi and Snyder,
[2015] and Gentzkow et al., [2015], respectively. This literature provides
good evidence that mass media have an impact on election outcomes and readers’
policy preferences. For the U.S., Gentzkow et al., [2011] report that the
opening of local newspapers boosts voter turnout. Drago et al., [2014] find
similar results for Italy. Chiang and Knight, [2011] show that a newspaper
endorsement for a presidential candidate shifts voting intentions in favor of
this candidate. Djourelova, [2020] shows, for the case of immigration and
border security, that the language used in newspapers can causally shift
readers’ policy preferences. Beyond the United States, Enikolopov et al.,
[2011] find that Russian voters with access to an independent television
station are more supportive of anti-Putin parties.444Prominent contributions
on the persuasive effects of the mass media around the world also include
Adena et al., [2015], DellaVigna et al., [2014], or Yanagizawa-Drott, [2014].
For surveys on the political effects of the mass media, see Prat and
Stroemberg, [2013] and Stroemberg, [2015].

Regarding Fox News in particular, DellaVigna and Kaplan, [2007] and Martin and
Yurukoglu, [2017] document that a quasi-experimental increase in exposure
leads to higher Republican vote shares [see also Ash et al.,, 2021]. Moreover,
it has also been shown that cable news can affect voter knowledge [Hopkins and
Ladd,, 2014, Schroeder and Stone,, 2015], fiscal policy decisions [Galletta
and Ash,, 2019], as well as behaviors during the COVID-19 pandemic and belief
in electoral conspiracy in the 2020 U.S. election [Bursztyn et al.,, 2021, Ash
et al.,, 2020]. None of these previous papers look at the influence of
partisan narratives using text analysis, nor do they look at effects on other
news outlets.

Our contribution to the debate on mass media persuasion centers around the
effects of news media outlets on each other. Recent contributions on cross-
media influence address the influence of social media on traditional media
[Cagé et al.,, 2020, Hatte et al.,, 2021]. To our knowledge, we are the first
to document how content from one media organization can spill over to other
media organizations. Hence, our work identifies a potential channel through
which partisan media affects political and social outcomes.

Local news outlets are pivotal for citizen engagement and political
accountability [e.g., Snyder and Strömberg,, 2010]. George and Waldfogel,
[2006] find that the market entry of a national media outlet (in their case,
the New York Times) causes local outlets to focus more on local coverage.
Martin and McCrain, [2019] show that the acquisition of U.S. local TV stations
by the national conglomerate owner Sinclair leads to an increased share of
national as opposed to local content. Further, Mastrorocco and Ornaghi, [2020]
document that these acquisitions by Sinclair reduce coverage of local crime
and subsequently lower crime clearance rates. We contribute to these debates
by analyzing how higher exposure to slanted national cable news changes local
news content.

Methodologically, our approach combines natural language processing (NLP),
machine learning, and causal inference, extending the use of NLP to understand
partisan influences in the media. The current literature on text-as-data
approaches to measure partisanship includes Gentzkow and Shapiro, [2010], Ash
et al., [2017], and Gentzkow et al., [2019], who analyze divisiveness in
congressional language. We link text-based methods with an instrumental-
variables framework to analyze the diffusion of political messaging across
media outlets. The methods could be useful for economists seeking to use text
in a causal framework. As detailed below, we address several issues in terms
of high dimensionality, lack of interpretability, and omitted variables.

More broadly, our work contributes to the long-lasting debate on the
importance of (un)biased media in democratic politics – a topic that has
become especially important in the current era of polarization in the U.S. and
beyond.

##  Data

This section enumerates our data sources. The data come from cable news
channels and from local newspapers. Our resulting panel is from 2005 through
2008, the years for which we can construct cable news viewership by locality.
Summary statistics for all variables are reported in Appendix Table A.2.

#### Local newspaper article excerpts.

Figure 1: Example of a local newspaper article snippet

Alameda Times-Star
County outlines ways to lower shelter hostility
8 March 2005

Can Alameda County blunt opposition to current plans to permit emergency
homeless shelters at hundreds of residential locations in unincorporated
communities? That appeared likely Monday as county planners suggested ways in
which shelters - such as in the land-use game Monopoly - would not
automatically pass go and neighbors could voice their approval or opposition.
[…]

The first ingredient in our analysis is a corpus of local newspaper articles.
Our data source is the news aggregation site NewsLibrary, from which we
scraped the headlines and first 80 words of all published articles for various
local U.S. newspapers for 2005-2008. With a set of scripts, we read through
the snippets and extract the newspaper name, the title and the plain text of
the article, as well as the date. An example of an article snippet is shown in
Figure 1. Our main dataset contains 16 million article snippets of on average
80 words each (starting from the beginning of the article) from 305 unique
newspaper titles. In the robustness checks, we use a larger dataset of 24
million articles from 682 titles. Appendix A.1 provides more information.

#### News show transcripts for FNC, CNN, and MSNBC.

Our second corpus is from cable news networks. We gather the news show
transcripts for FNC, CNN, and MSNBC from LexisNexis. The corpus includes
transcripts from around 40,000 episodes of prime time shows for the three
networks for 2005-2008. We have a series of scripts that read through the
transcripts to filter out metadata and other non-speech content.

As mentioned, the newspaper article snippets contain approximately the first
80 words of the article. The transcripts tend to be much longer. To make the
corpora more comparable, we segment the transcripts into shorter 80-word
snippets to match the length of the newspaper article snippets.

#### Newspaper-level circulation data.

Next, we match each local newspaper outlet to one or more counties. We use
audited county-level circulation data from the Alliance for Audited Media
(AAM), which is available for around 305 unique newspapers (that also appear
in the NewsLibrary and the Nielsen ratings data). We thus have 3,781
observation units at the newspaper-county level (see Section 4) for our main
analyses. The AAM also provides information on the headquarters location of
the newspapers. We will exploit this information to study heterogeneous
effects in Section 6.

Appendix A.2 describes an alternative method to match newspapers to counties
(not relying on the AAM data). This procedure results in 682 observation
units, which we use in robustness checks.

#### Channel positions and viewership.

From Nielsen we have yearly data on channel positions and ratings for Fox News
Channel, CNN, and MSNBC. These are the same as the data used by Martin and
Yurukoglu, [2017]. First, we have the channel lineup for all the U.S.
broadcast operators and the respective zip code areas served. Second, we have
viewership information representing the share of individuals tuned in to each
channel by zip code. This value is proportional to the average number of
minutes spent watching a channel per household. As the original data are at
the zip code level, we follow Galletta and Ash, [2019] and aggregate both the
ratings and the channel positions at the county level. Specifically, we create
county-year average channel positions, weighting the observations by
population size in the zip code, while we weight ratings by the number of
survey individuals in the zip code according to Nielsen. These variables are
then collapsed at the county level by computing the mean across the years
2005-2008.

#### Other demographic covariates.

Finally, we have a rich set of demographic covariates from the 2000 census.
These variables are measured at the zip code level. To get the aggregate value
for the county, we weight them by zip code population. Appendix Table A.2
lists these variables along with summary statistics.

##  Measuring Media Slant

This section describes how we construct the language measures to be used as
outcome variables in our regression analysis. We aim at capturing textual
similarity between the newspaper article snippets on the one hand and TV show
transcripts on the other. Therefore, we implement a supervised machine
learning approach to predict if newspaper article language resembles that from
a particular TV station (FNC or CNN and MSNBC).555The approach is related to
Gentzkow et al., [2019], who also use a regularized linear model with n-gram
inputs. Our different approach reflects a different scientific objective.
Gentzkow et al., [2019] are interested in measuring the level of polarization
between groups in language. We are interested in forming a predicted
probability of the source of a document for scoring influence in a second
corpus. Other related methods are Peterson and Spirling, [2018] and Osnabrügge
et al., [2021].

###  Text pre-processing and featurization

First, we preprocess all texts (newspaper articles and TV station
transcripts). We convert them to lower case and remove non-meaningful
stopwords (like and or or), all non-letter characters, and extra white-spaces.
Second, for each word, we perform stemming (employing the Porter stemming
algorithm). Finally, we form bigrams (two-word phrases) from the word stems.

Let $M$ be the set of documents (snippets indexed by $m$) from the transcripts
corpus. We group CNN and MSNBC together (for a simple notation, we refer to
the CNN/MSNBC label as CNN). Thus the label we will predict is $FNC_{m}$: For
each transcript snippet $m$, $FNC_{m}=1$ if it comes from a Fox News
transcript and $FNC_{m}=0$ otherwise (if it comes from CNN/MSNBC). We produce
a balanced sample of documents, with half from Fox and half from
CNN/MSNBC.666We have fewer snippets from FNC than from CNN/MSNBC. Thus, we
randomly under-sample the snippets from the CNN/MSNBC corpus to match the
number of snippets from FNC.

Let $V_{k}$ give the vocabulary of bigrams used by a given channel
$k\in\\{FNC,\ CNN\\}$. Let $F_{k}^{b}$ be the frequency of bigram $b$ on
channel $k$. We construct $V_{FNC}$ and $V_{CNN}$ and then intersect the two,
imposing the condition that any bigram $b$ must appear more than 20 times in
both corpora. The resulting set of bigrams is denoted as

$V=\\{b\in V_{FNC}\ \&\ b\in V_{CNN}\ |\ F^{b}_{FNC}>20\ \&\
F^{b}_{CNN}>20\\}$

The frequency threshold serves to exclude infrequent bigrams which are highly
distinctive for a given channel, but carry little substantive political or
topical information. This procedure produces a vocabulary $V$ with 65,000
bigrams.777Previous work has shown that supervised learning models using
n-grams are rarely sensitive to the specific choices in pre-processing and
featurization [e.g., Denny and Spirling,, 2018].

###  Classifying transcripts by TV source

Now we will train a machine learning classifier to predict whether a
transcript snippet $m$ comes from FNC or CNN/MSNBC. We split the corpus into
80% training data and 20% test data. We build the classifier in the training
set and evaluate its performance in the test set.

We take two steps to further pre-process the features, both using the training
set to ensure a clean evaluation in the test set. First, we do supervised
feature selection to reduce the dimensionality of the predictor matrix. Out of
the 65,000-bigram dictionary, we select the 2,000 most predictive features
based on their $\chi^{2}$ score for the true label $FNC$. Second, we scale all
predictors in $S$ to variance one (we do not take out the mean, however, as
then we would lose sparsity). Let $S$ be the vector of selected and scaled
features, indexed by $b$. Let $B^{b}_{m}$ be the frequency of bigram $b$ in
transcript $m$ (and $B_{m}$ the vector of frequencies for transcript $m$, of
length $|S|=2000$).

Our classification method is a penalized logistic regression [Hastie et al.,,
2009]. We parametrize the probability that a transcript is from Fox News as

$\widehat{FNC}_{m}=\Pr[FNC_{m}=1|B_{m}]=\frac{1}{1+\exp(-\psi^{\prime}B_{m})}$

where $\psi$ is a 2000-dimensional vector of coefficients on each feature. The
L2-penalized logistic regression model chooses $\psi$ to minimize the cost
objective

$J(\psi)=-\frac{1}{M^{*}}\sum_{m=1}^{M^{*}}\left(FNC_{m}\log(\widehat{FNC}_{m})+(1-FNC_{m})\log(1-\widehat{FNC}_{m})\right)+\lambda|\psi|_{2}$
(1)

where $M^{*}$ gives the number of documents in the training sample.

The rightmost term in Equation (1) is the regularization penalty. We use Ridge
regularization, as indicated by the L2 norm $|\cdot|_{2}$. The Ridge
regularization mitigates over-fitting of the training set by shrinking
coefficients towards zero.888We obtained similar test-set accuracy when using
an L1 (Lasso) penalty. Regularization strength is calibrated by the
hyperparameter $\lambda\geq 0$, selected using five-fold cross-validated grid
search in the training set. The optimal penalty in our data is
$\lambda^{*}=2$, although we got almost identical performance with larger or
smaller penalties.

Table 1: Test-Set Prediction Performance for Identifying Cable News Source

| Predicted CNN | Predicted FNC
---|---|---
Actual CNN | 38.3% (235K) | 11.7% (72K)
Actual FNC | 15.0% (92K) | 35.0% (215K)

_Notes:_ Confusion matrix for test-set predictions. Top left gives true
positives for the CNN/MSNBC class; bottom right gives true positives for the
FNC class; top right gives false negatives for CNN/MSNBC; bottom left gives
false negatives for FNC.

We evaluated classifier performance in the test set, obtaining an accuracy of
0.73 (with a standard deviation of 0.02 across five folds). This performance
is much better than guessing (which would produce an accuracy of 0.5 in the
balanced sample) and comparable with other works in this literature.999For
example, the prediction accuracy for partisan affiliation in U.K. parliament
obtained by Peterson and Spirling, [2018]: They obtain an accuracy of between
0.6 and 0.8, depending on the time period in the data. Kleinberg et al.,
[2017] obtain an AUC of 0.71 in predicting recidivism from criminal defendant
characteristics. The confusion matrix in Table 1 demonstrates the good
performance in terms of precision and recall across the two categories. The
on-diagonal cells have most of the mass and are quite balanced.101010Our
finding on systematic differences in FNC and CNN reporting aligns well with
previous case study-based evidence. Harmon and Muenchen, [2009] show that FNC
and CNN cover a range of politically salient topics differently, such as the
Iraq war in the early 2000s.

Figure 2: Model Predicted Probabilities Accurately Reproduce Test-Set
Distribution

_Notes:_ Calibration plot for our predictions. The figure shows – for the test
set – the binned means (rates) of coming from the Fox News transcripts,
conditional on the predicted probability from our model. The 45 degree line
indicates how the line would look if the model replicated the distribution in
the data perfectly – for example, for the set of observations with a 30%
predicted probability, exactly 30% of them are truly from Fox News
transcripts. Similarly, this holds for all twenty bins (of 5% increments). As
can be seen in the figure, the fit is remarkably good. The conditional
predicted rates are almost perfectly on top of the 45 degree line.

Figure 2 reports the calibration plot for our predictions. The figure shows –
for the test set – the binned means (rates) of coming from the Fox News
transcripts, conditional on the predicted probability from our model. The 45
degree line indicates how the line would look if the model replicated the
distribution in the data – for example, for the set of observations with about
30% predicted probability, we see that about 30% of them are truly from Fox
News transcripts. Similarly, this holds for all twenty bins (of 5%
increments). As can be seen in the figure, the fit is remarkably good. The
conditional predicted rates are almost perfectly on top of the 45 degree line.

Another criterion for evaluating the model is how well it compares with human
judgment. To make such a comparison, we asked a team of human annotators (U.S.
college students) to guess whether 80-word TV transcript snippets come from
FNC or CNN/MSNBC. The annotators are between 73% and 78% accurate in their
guesses, and they agree on the annotation about 59% of the time. Thus, our
machine learning model is quite similar in performance to human annotations.
The 80-word snippets do contain significant information about the source
network, and our text-based model captures it. See Appendix B.2 for more
details on the human validation.

Table 2: Distinctive phrases associated with Fox News and CNN/MSNBC

FNC | CNN/MSNBC
---|---
al qaida | eastern pacif
homicid detect | chief medic
war stori | report baghdad
captur terrorist | person world
sean hanniti | anderson cooper
far left | polit analyst

_Notes:_ Examples of bigrams with positive (predictive for FNC transcripts) or
negative (predictive for CNN/MSNBC) coefficient values in the penalized
logistic regression (of a label equaling one for FNC snippets, and zero for
CNN/MSNBC snippets on the bigrams used in a snippet).

To understand better how the model is making these predictions, we examine the
bigram features that are most important for its classification choices. An
advantage of logistic regression in this respect is its interpretability: The
estimated coefficients of the trained model, $\hat{\psi}_{b}$, provide a
ranking across the 2,000 predictive bigrams in terms of their relative
contribution to the predictions. Because the predictors are standardized to
the same variance, the coefficients are comparable and roughly interpretable
as the relative marginal effect of the associated bigram on the predicted
probability that a document is from Fox News.

Table 2 shows examples of bigrams with positive (predictive for FNC
transcripts) or negative (predictive for CNN/MSNBC) values of
$\hat{\psi}_{b}$.111111Appendix Table B.1 includes a longer list. Prominent
figures like Sean Hannity (predictive of FNC) or Anderson Cooper (predictive
of CNN/MSNBC) appear among the bigrams. Fox bigrams allude to intuitively
conservative priorities such as the troops, crime, terrorism, and (implied)
extremism of political counterparts (“far left”). CNN/MSNBC bigrams have a
more liberal flavor, with mentions of health-policy related tokens and
emphasis on international perspectives. Interestingly, both sets of features
mention to similar topics or events (e.g., war). This already suggests that
the differences in cable news rhetoric are, at least in part, due to framing
of topics, rather than just topic choices (we investigate this question in
Section 6.1).

###  Text similarity between newspapers and TV stations

Having validated that our model captures useful information, we can now take
it to the newspaper snippets to score their relative similarity to each cable
news network. Let $N$ be the set of newspaper article snippets (indexed by
$n$) and $A_{n}^{s}$ the frequency of predictive bigram $s$ in snippet $n$.
$A_{n}$ is the vector of frequencies (of length $S$) for article $n$. Our
prediction of $FNC$, $\widehat{FNC}$, for snippet $n$ is hence:

$\widehat{FNC}_{n}=\Pr[FNC=1|A_{n}]=\frac{1}{1+\exp(-\hat{\psi}^{\prime}A_{n})}$

which gives a predicted probability (between zero and one) for how likely each
newspaper snippet was generated by Fox News.

Note that, as newspaper article snippets do not come with any label, we cannot
evaluate accuracy in predicting newspaper article language. However, we
provide some interpretive validation in Appendix C, where we list the news
article snippets with the highest and lowest $\widehat{FNC}_{n}$. We find that
the topical and rhetorical content of the article snippet reflects intuitions
about the ideological commitments of the networks. In Appendix Table B.2, we
see that FNC-related articles include defenses of U.S. military involvement in
Africa, crime, Bush’s opposition to troop withdrawals, and a Supreme Court
case about the Second Amendment (right to bear arms). Articles that are
closest to CNN/MSNBC (Appendix Table B.3) are about campus groups supporting
gay rights, the AIDS crisis in Africa, President Bush’s responsibility for the
financial crisis, and HIV in the gay community.

We now have $\widehat{FNC}_{n}$ as a similarity measure between TV channel
language and the language in newspaper article $n$. To link the article-level
data to the other datasets at the newspaper level $i$ or the county level $j$,
we aggregate by taking the mean values of the contained news articles. Hence,
we define $\text{Slant}_{ijs}$ as our newspaper-level slant measure, equal to
the average probability of snippets by newspaper $i$ (in county $j$ in state
$s$) to be FNC-like.

For the main analysis, we combine our text similarity measures with data on
cable news viewership. In our main dataset, there are 305 newspapers
circulating in 12.4 counties on average (the median is six counties),
resulting in the aforementioned 3,781 observations. Appendix Figure B.1 shows
the distribution of linguistic similarity with FNC for the 305 unique
newspapers in our main dataset (i.e., $\mathrm{Slant}_{ijs}$).

###  Topic model

When studying the mechanisms of slant contagion in Section 6, we require topic
labels for the transcripts and news articles. To learn topics directly from
our parallel corpus, we use the Latent Dirichlet Allocation (LDA) topic
modeling approach of Blei et al., (2003). LDA is the standard approach for
topic modeling in social-science applications (e.g., Hansen et al.,, 2018;
Bybee et al.,, 2020). It represents documents as combinations of a finite
number of latent topics, while the topics are characterized by a distribution
over words.

We build two different topic models: one based on the TV transcripts and one
based on the newspaper article snippets. For both models, we specify 128
topics and train LDA on a random sample of 1 million documents (TV transcript
snippets or newspaper article snippets, respectively).121212We use the online
variational Bayes (VB) implementation by Hoffman et al., (2010). To select the
number of topics, we started with 32 topics and doubled the number of topics
until they became largely interpretable to humans. 128 topics ended up working
well for both corpora. The topics are labeled manually based on the associated
words (see Appendices B.5 and B.6).

We then use the trained models to assign topic(s) to all transcripts and all
newspaper articles. The TV-transcripts-based topic model is used for the
analysis of framing in Section 6.1. The newspaper-articles-based topic model
is used to label articles as either local or non-local, as discussed further
in Section 6.2.

##  Econometric Framework

Our main hypothesis is that higher viewership of a cable channel in a county
will cause the local newspapers to feature content similar to that channel’s.
This section outlines our method to test for this causal relationship.

###  Instrumental variables specification

The empirical strategy uses an instrumental variable regression. The main
outcome variable is $\text{Slant}_{ijs}$, the textual similarity to Fox News
for newspaper $i$ circulating in county $j$ in state $s$ (see Section 3.3
above). $\mathrm{Slant}_{ijs}$ is a relative measure. It is interpretable as
the average predicted probability that the articles of a newspaper came from
FNC, rather than CNN or MSNBC. Correspondingly, we are interested in the
causal effect on this outcome of relative local viewership of FNC compared to
CNN/MSNBC. Hence, we specify our main treatment variable,
$\text{Viewership}_{js}$, as the county-level Fox News viewership relative to
the averaged county-level CNN and MSNBC viewership: FNC Viewership -
0.5$\times$(MSNBC viewership+CNN viewership).131313This specification for the
treatment is different from Martin and Yurukoglu, [2017] and reflects our
different outcome and research question. Still, we will report the more
standard specification of just (non-relative) FNC viewership in robustness
checks.

We specify the relationship between slant and viewership using a linear model:

$\text{Slant}_{ijs}=\alpha_{s}+\theta\text{Viewership}_{js}+X_{ijs}\beta+\epsilon_{ijs}$
(2)

where $\theta$ is the causal parameter of interest. Besides the outcome and
treatment, the regression includes state fixed effects ($\alpha_{s}$), a
vector of county and newspaper controls ($X_{ijs}$), and an error term
($\epsilon_{ijs}$). The set of covariates $X_{ijs}$ varies across
specifications, but can include demographic controls (see Appendix Table A.2),
channel controls (population share with access to each of the three TV
channels), and generic newspaper language controls (vocabulary size, avg. word
length, avg. sentence length, avg. article length).

Estimating Equation (2) using OLS is likely to produce biased estimates of
$\theta$. There are many political and economic factors that may correlate
with both Fox News viewership and the use of Fox-like language by local
newspapers – in particular, any pre-existing ideological preferences of the
county. The potential correlation between $\text{Viewership}_{js}$ and
$\epsilon_{ijs}$ due to these confounders would add bias to OLS coefficients.
To address this problem, we take an instrumental-variables approach.

Inspired by Martin and Yurukoglu, [2017], we use cable network channel
positioning to construct an instrument $\text{Position}_{js}$ that affects
$\text{Viewership}_{js}$ but is otherwise unrelated to any factors affecting
$\text{Slant}_{ijs}$. As first shown by Martin and Yurukoglu and since used in
a number of papers [e.g., Galletta and Ash,, 2019], there is arbitrary
variation in cable channel positioning across U.S. localities. This channel
positioning leads to exogenous shifts in viewership because television
watchers spend more time on networks with lower channel positions. Thus, we
instrument viewership using channel position. Because our treatment is a
relative measure of FNC viewership compared to CNN/MSNBC viewership, we
specify the instrument $\text{Position}_{js}$ as the county-level FNC channel
position relative to the averaged channel position of MSNBC and CNN: FNC
Position - 0.5$\times$(MSNBC Position+CNN Position).141414As mentioned above,
we will also report the more standard specification of just (non-relative) FNC
channel position in robustness checks. The first stage estimating equation is

$\text{Viewership}_{js}=\alpha_{s}+\delta\text{Position}_{js}+X_{ijs}\beta+\eta_{ijs}$
(3)

where the other terms are as above.

The first stage (3), combined with Equation (2), can be estimated with two-
stage least squares (2SLS) to procure causal estimates for the local average
treatment effect $\theta$. To facilitate the interpretation of the
coefficients, we standardize the instrument, endogenous regressor, and outcome
by dividing the original values by the standard deviations. Standard errors
are two-way-clustered by newspaper and county.151515We show that our results
are also significant when standard errors are clustered by state.

Our estimates use weighted regressions. Most newspapers serve more than one
county, yet the circulation across counties is unevenly distributed. To
account for how much each newspaper is influenced by the channel position in
its associated counties, newspaper-county observations are weighted by a
newspaper’s circulation in that county.161616We demonstrate robustness to
other weighting schemes, including a variant of the approach in Martin and
Yurukoglu, [2017] which weights by the number of households in a locality
surveyed by Nielsen.

###  Instrument validity

2SLS requires relevance in the first stage. Figure 3 visualizes the first-
stage relation between the FNC channel position (relative to the averaged
position of CNN and MSNBC) and FNC viewership (also relative to
CNN/MSNBC).171717See Appendix Table C.1 for coefficients and standard errors
in tabular format. The relationship is significantly negative and similar
without controls (panel a) and with the addition of controls (panel b). A one-
standard-deviation decrease in the relative channel position (11 positions in
the lineup) increases relative viewership by about 10% of a standard deviation
(0.041 rating points). A one-tenth of a rating point equals roughly 45 minutes
per month of (additional) viewership per household. Hence, our first-stage
coefficient means that decreasing the channel position of FNC by 11 (while
holding the positions of CNN and MSNBC constant) would increase the viewership
of FNC and decrease the viewership of CNN/MSNBC such that the FNC-to-CNN/MSNBC
viewership difference goes up by 22 minutes per month. In the tables below, we
report Kleinbergen-Paap cluster-robust first-stage F-statistics and they are
consistently above 30, indicating a well-powered first stage.

Figure 3: First Stage: Cable Channel Position and Cable News Viewership

(a) No controls

(b) With controls

_Notes:_ Binned scatterplots (16 bins) of standardized viewership of
FNC-0.5(CNN+MSNBC) against standardized position of FNC-0.5(CNN+MSNBC). Cross-
section with newspaper-county-level observations weighted by newspaper
circulation in each county. On the left, state fixed effects are included. On
the right, state fixed effects, as well as demographic controls (see Appendix
Table A.2), channel controls (population share with access to each of the
three TV channels), and generic newspaper language controls (vocabulary size,
avg. word length, avg. sentence length, avg. article length) are included. In
grey (next to the axes), we show the distributions of the underlying
variables.

Beyond relevance, 2SLS imposes three requirements for consistent estimates.
The first two, exclusion and monotonicity, are not problematic in our context.
Exclusion requires that the channel position affects local news reporting only
through its effect on cable news viewership. We believe this is a reasonable
assumption in our context. The monotonicity assumption is that the channel
position influences news viewership in the same direction for all counties. It
is reasonable to assume that increasing the channel position would not
systematically increase viewership.

The third assumption, exogeneity, is that $\text{Position}_{js}$ is
uncorrelated with $\epsilon_{ijs}$. More concretely, we need that the channel
position is not endogenously selected with county-specific preferences for
conservative or liberal news reporting. The main identification problem is
that channel positions could be allocated strategically in response to local
factors correlated with conservative news messaging.

Martin and Yurukoglu, [2017] provide a detailed discussion and a set of checks
supporting the exogeneity assumption. Based on qualitative research, they
highlight that channel positions have an important arbitrary, historical
component, with significant inertia and path dependence. Quantitatively, the
instrument is not correlated with Republican vote shares before the
introduction of Fox News Channel. Galletta and Ash, [2019] report a number of
additional checks at the county level showing the instrument to be unrelated
to demographic characteristics that predict policy preferences or news channel
viewership.

We apply the same identification checks to the counties in our sample
(newspaper-county-level data). As in Galletta and Ash, [2019], we use linear
regressions with demographic characteristics and state fixed effects as
covariates to predict viewership and newspaper content. Specifically, we
obtain predictions related to the endogenous regressor (viewership) and to the
outcome (the probability of newspaper content to be Fox-like). These
predictions summarize the variation in viewership and news content that is due
to pre-existing cultural, economic, and political characteristics of these
counties.

Table 3: Identification checks: Instrument Uncorrelated with Relevant
Covariates

| Reduced form
---|---
| Viewership |  | Viewership |
| FNC* | Slantijs* | FNC* | Slantijs*
| (absolute) |  | (rel. to CNN/MSNBC) |
| (1) | (2) | (3) | (4)
FNC position (absolute) | -0.022 | 0.017 |  |
| (0.043) | (0.013 |  |
FNC position |  |  | 0.006 | -0.006
(rel. to CNN/MSNBC) |  |  | (0.025) | (0.006)
N observations | 3781 | 3781 | 3781 | 3781
State FE | X | X | X | X

  * 1.

Notes: Estimates are based on OLS with newspaper-county-level observations
weighted by newspaper circulation in the respective county. Asterisks (*)
indicate linear predictions: The dependent variable is the predicted
viewership of FNC in column (1), the predicted newspaper language similarity
with FNC in columns (2) and (4), and the predicted viewership of FNC relative
to averaged MSNBC and CNN viewership in (3). The predictions are derived from
regressions that include the full set of demographic controls and state fixed
effects. Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

We then regress these predictions on different definitions of our instrument
$\text{Position}_{js}$. Table 3 shows the results of this identification
check. Columns 1 and 2 document that there is no significant relationship
between the absolute position of Fox News and the predicted values for
viewership or newspaper content. Columns 3 and 4 show that there is no
significant relationship between relative FNC channel position and the
respective predicted values.181818As an additional placebo we estimate our
main specifications but using as the predicted text similarity to cable news
using local newspaper articles from 1995 and 1996 (pre-FNC/MSNBC). The placebo
check estimates, reported in Appendix Table C.2, show no significant effects.
Reassuringly, there was not a pre-existing Fox-like language dimension in
locations that later had a lower Fox channel position. Note that the placebo
regressions are based on fewer observations than the main results because some
newspaper titles are not yet available in NewsLibrary in 1995 and 1996, or
their circulation data is not yet available from the AAM. Our main results
remain qualitatively similar and are significant at the p$<$0.01 level if we
only use the observations entering the placebo regression.

Overall, these results support the view that channel positions are not
selected or adapted to county characteristics that are otherwise important for
our endogenous regressor or outcome. The placebo nulls provide additional
support for instrument validity and more generally for our empirical strategy.

##  Results

This section first presents the main results, which include reduced form and
two-stage-least-squares estimates. Second, it reports robustness checks.

###  Main results

Figure 4 visualizes the reduced form relationship between the FNC channel
position (relative to the averaged MSNBC and CNN position) and local newspaper
content similarity to FNC. In the left part of the Figure (panel a), the
outcome and the instrument are residualized on state fixed effects. On the
right-hand side (panel b), we additionally include demographic controls,
channel controls (share of households with access to each of the three
channels), and generic newspaper language features (vocabulary size, average
word length, average sentence length, and average article length).191919As
discussed further in Section 6.1 below, we find that the instrument does not
have a direct effect on these language features (see Appendix Table C.13).
There is a clear downward relationship, suggesting that easier access to FNC
is associated with more FNC-like language in the local county
newspapers.202020See Appendix Table C.4 for the reduced-form results in
tabular format.

Figure 4: Reduced form: Cable News Channel Position and Local Newspaper
Content Similarity

(a) No controls

(b) With controls

_Notes:_ Binned scatterplots (16 bins) of standardized textual similarity with
Fox News against standardized position of FNC-0.5(CNN+MSNBC). Cross-section
with newspaper-county-level observations weighted by newspaper circulation in
each county. On the left, state fixed effects are included. On the right,
state fixed effects, as well as demographic controls (see Appendix Table A.2),
channel controls (population share with access to each of the three TV
channels), and generic newspaper language controls (vocabulary size, avg. word
length, avg. sentence length, avg. article length) are included. In grey (next
to the axes), we show the distributions of the underlying variables.

Table 4: Cable News Effects on Newspaper Content (2SLS)

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.314*** | 0.311*** | 0.318**
| (0.114) | (0.113) | (0.126)
K-P First-Stage F-stat | 36.553 | 36.298 | 34.147
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC), $\text{Slant}_{ijs}$. The
right-hand side variable of interest is instrumented FNC viewership relative
to averaged CNN and MSNBC viewership: Viewership (FNC - 0.5(MSNBC - CNN))).
All columns include state fixed effects and demographic controls as listed in
Appendix Table A.2. Column 2 also includes channel controls (population shares
with access to each of the three TV channels). Column 3 controls for generic
newspaper language features (vocabulary size, avg. word length, avg. sentence
length, avg. article length). Standard errors are multiway-clustered at the
county and at the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$
0.05, *** p $<$ 0.01.

Table 4 shows two-stage-least-squares estimates of the effect of higher FNC
viewership on newspaper language similarity with FNC (the average probability
that a snippet from a newspaper is predicted to be from FNC based on the
bigrams it contains). The right-hand side variable of interest is instrumented
FNC viewership relative to averaged CNN and MSNBC viewership. All columns
include state fixed effects and demographic controls. Column 2 also includes
controls for the share of households with potential access to each of the
three TV channels. Column 3 additionally controls for generic newspaper
language features.

In all three columns, the estimated treatment effects are positive and
statistically significant. The magnitudes across columns are highly similar,
they range from 0.31 in columns 1 and 2 to 0.32 in column 3. This means that
the channel and language controls do not change the estimates relative to the
baseline in column 1 where only state fixed effects and demographic controls
are included. All variables are standardized, so the interpretation is as
follows: if Fox News viewership (relative to averaged CNN and MSNBC
viewership) increases by one standard deviation in county $j$ where newspaper
$i$ circulates, the similarity of $i$’s content with FNC increases by 0.31
standard deviations.212121As already mentioned, OLS estimates of the
relationship between FNC viewership and FNC-related language in local
newspapers do not have a causal interpretation. These are reported in Appendix
C.3. Overall, we find that OLS coefficients have a positive sign, as with the
2SLS coefficients, though they are smaller in magnitude and only significant
for absolute FNC viewership. Alternatively, a one-standard-deviation decrease
in the relative FNC channel position (11 relative positions) would increase
slant by 0.03 standard deviations.

To interpret the magnitudes, note that the average difference of slant between
an FNC transcript snippet and a CNN/MSNBC transcript snippet in standardized
units is 0.99 (the raw difference, in predicted probabilities, is 0.21). Given
the estimated 2SLS coefficient (0.31), we can say that a one-standard-
deviation decrease in the relative FNC channel position (11 positions) will
shift slant towards FNC by about 3 percent of the difference between FNC and
CNN/MSNBC. Regardless, we emphasize that 2SLS estimates identify a local
average treatment effect (LATE), so they are externally valid only for the set
of newspaper-county observations that are responsive to the channel-position
instrument.

###  Robustness checks

This section provides various checks to assess the robustness of the main
results. First, we show robustness of the results to alternative samples.
Appendix Table C.5 replicates the baseline estimates but only considering
newspaper-county observations where the county coincides with where the
immediate owner of the newspaper is based.222222We assign the city where the
owner of the local newspaper is based to a U.S. county, using data from the
Alliance for Audited Media (see Section 2). We focus on immediate owners –
that is, we do not consider the location of the parent company for newspapers
that are owned by a conglomerate. The effect size is more than doubled
relative to the entire sample and still statistically significant despite a
smaller sample.232323On the other hand, as shown in Appendix Table C.6, the
effect is smaller and no longer significant when excluding headquarters
counties.

Second, we show robustness to alternative weighting specifications. In
Appendix Table C.8, we show that the results are robust to weighting by
circulation from the pre-FNC/MSNBC era (1995), meaning that our main results
are not driven by potential changes in circulation due to cable news
exposure.242424We do not use the pre-FNC/MSNBC county-level circulation data
in our main analysis because it is available for fewer outlets, resulting in
half the sample size. Our baseline specification (contemporary circulation
weights) is robust to using the subsample of observations where 1995
circulation is available. Next, we weight observations by relative circulation
shares by county, multiplied by the number of surveyed individuals for each
county by Nielsen (Appendix Table C.9). In using the number of surveyed
individuals, we follow Martin and Yurukoglu, [2017].252525Similarly, we also
weight observations by relative circulation shares by county, multiplied by
county population (Appendix Table C.10). The results are again positive and
statistically significant.

Third, we replicate the main results using different specifications for the
instrument (Appendix Table C.11). Instead of FNC viewership relative to CNN
and MSNBC viewership combined, we look at FNC viewership relative to CNN and
MSNBC separately (columns 1/2 and columns 3/4, respectively), or FNC
viewership on its own (columns 5 and 6). The estimates are positive and
overall consistent with our main results, yet not statistically significant in
the FNC-vs-CNN specification.

Fourth, we replicate our main results, but relying on a different matching of
newspapers to counties. We assign each newspaper to a main county based on its
name and other metadata, producing a larger sample but with less detailed
information on circulation (see Appendix Section A.2 for details on this
approach). The results are fully robust (Appendix C.6).

Finally, Appendix C.10 reports two additional checks. Our main results are
still statistically significant when clustering by state rather than by county
and newspaper (Appendix Table C.12). The results are consistently
statistically significant when dropping each newspaper individually (Appendix
Figure C.2).

##  Heterogeneous Effects and Mechanisms

This section provides some additional supporting results to better understand
the mechanisms for the contagious slant effect. First, we distinguish topics
from framing. Second, we check whether there is an effect for local news or
just for national news. Third, we show some evidence that FNC has had a
polarizing effect on local newspapers.

###  Slant effects are mostly driven by framing rather than topics

A first mechanism question is what features of newspaper language are changing
in response to cable news. The politicized nature of the bigrams predictive of
FNC versus MSNBC/CNN (see Section 3.2) suggests that the diffusing slant
consists of ideological or political content, but one alternative we would
like to check is whether more stylistic (that is, apolitical) features of the
newspaper language change in response to higher FNC exposure. In Appendix
Table C.13, we show there is no effect of FNC exposure on generic style
features (vocabulary size, average word length, average sentence length, and
average article length).

Within the space of political content, we explore whether the changes in the
newspaper language are driven by changes in the topics or in the framing. It
could be that cable news changes the news agenda, so that local newspapers
cover the same types of stories (e.g., writing more about the military, rather
than about poverty). Or instead, it could be that the broad stories are
constant, but framed differently (e.g., writing more supportively of the
military, or more dismissively of poverty). To check for the relevance of
topics, we re-run our penalized logistic regression (see Section 3.2), but now
with the topic shares instead of the bigrams as features, to predict whether a
TV transcript snippet is from FNC or CNN/MSNBC (for details on the topic
model, see Section 3.4). Using this feature set, we obtain a test set accuracy
of just 55%, much lower than the 73% with bigram features. That is, our
classifier cannot distinguish the cable networks using broad topics. This
finding is an initial indication that our measure of slant is primarily due to
framing.

To further explore the relevance of framing, we run our main regressions but
adding topic shares from the newspaper articles as an additional set of
covariates.262626That is, we use the topic model trained on cable news
transcripts to assign to each newspaper article its topic shares for each of
the 128 topics. We then aggregate the topic shares at the newspaper level,
giving us an indication of how much a newspaper generally focuses on a given
topic. As shown in Appendix Table C.14, the estimates are largely unchanged,
providing descriptive evidence that the changes in local newspaper content are
predominantly driven by framing. However, the coefficients are around 20%
smaller in magnitude, suggesting that broad topics do play a minor
role.272727Our preferred specification (third column; with all controls)
implies that if Fox News viewership (relative to averaged CNN and MSNBC
viewership) increases by one standard deviation in county $js$ where newspaper
$ijs$ circulates, the FNC similarity of the newspaper content increases by
0.26 standard deviations (the same coefficient is 0.32 in Table 4 without
topic controls).

###  Cable news media slant influences local news content

A second mechanism question on slant contagion is what parts of the newspaper
content are shifting to be more like cable TV news. One possibility is that
slant diffusion works via production costs, making it cheaper for local news
outlets to produce articles by borrowing content (either directly or by
picking up the same stories that the channels cover). Another possibility is
that slant diffusion works by shifting the news preferences of readers, in
which case the slanted content would also spill over into originally produced
material.282828The features of the institutional setting make direct influence
of journalists, either through production costs or ideological preferences, an
unlikely mechanism for the observed 2SLS effects. A journalist can easily
access cable news and borrow material, regardless of local channel position.
Because journalists are relatively sophisticated news consumers, they
generally have strong pre-existing news preferences and would not be nudged by
the instrument. Martin and Yurukoglu, [2017] discuss this issue in the context
of voting, where one would expect cable news to influence swing voters who
don’t have a strong partisan commitment. Ash and Poyker, [2019] make a similar
point in the context of judges: cable news influences criminal sentencing
through voters and judicial elections, rather than persuading judges directly.
Local news is a content category where direct borrowing of material is not
possible, given the national focus of the cable news channels. Thus, to
elucidate this issue we study whether cable news exposure influences articles
in the local news section.

To distinguish local news from non-local (that is, national or international)
news, we proceed as follows. For every topic out of the 128 topics, we
manually label whether the topic is more likely to cover local as opposed to
non-local news (see Appendix Section B.6). We then impose the criterion that a
newspaper article snippet is classified as local news if, cumulatively, more
than 50% of its topic share(s) cover topics labelled as local. We validate
this approach as capturing local news content via blind annotation of 2,000
newspaper article snippets.292929We recruited annotators on Upwork, as we did
for the human validation of the slant measure (see Appendix Section B.2). The
annotators were not given any topic information; they simply read the
newspaper article snippet. 74 out of the 2,000 articles could not be labelled
as either local or non-local (it was unclear). We found that the topic-based
predictions come with an accuracy of 81% relative to the human annotation.
Since most news is local news, we also check other metrics for the local
category. The F1-score is 86%, while precision and recall are 88% and 84%,
respectively.

Table 5: Cable News Effects on Newspaper Content: (Non-)Local News

Dep. variable: | Local shareijs | Slantijs | Slantijs
---|---|---|---
FNC viewership (rel. CNN/MSNBC) | 0.039 | 0.377*** | 0.295**
| (0.032) | (0.134) | (0.131)
K-P First-Stage F-Stat | 34.147 | 34.147 | 34.147
N observations | 3781 | 3781 | 3781
Non-local articles |  | X |
Local articles |  |  | X
State FE | X | X | X
Demographic controls | X | X | X
Channel controls | X | X | X
Newspaper language controls | X | X | X

  * 1.

Notes: 2SLS estimates, only considering local news articles. Cross-section
with newspaper-county-level observations weighted by newspaper circulation in
each county. In the first, column, the dependent variable is the share of
articles in a newspaper categorized as local. In column 2 and 3, the dependent
variable is newspaper language similarity with FNC (the average probability
that a snippet covering local news from a newspaper is predicted to be from
FNC). In column 2, we only include non-local newspaper articles when
aggregating the slant measure at the newspaper-level. In column 3, only local
newspaper articles are considered. The right-hand side variable of interest is
FNC viewership relative to averaged CNN and MSNBC viewership. All columns
include state fixed effects and demographic controls as listed in Appendix
Table A.2, channel controls (population shares with access to each of the
three TV channels), and controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

To analyze the local-news issue, Table 5 replicates our main regression
specification but using three alternative outcomes. Column 1 shows the effect
of instrumented FNC viewership (relative to CNN and MSNBC) on the share of
local news. There is no effect, adding further evidence that cable-news
exposure does not change the broad topics covered. Next, column 2 shows that
there is a positive and significant effect on non-local (national and
international) news, with a coefficient even larger than our main estimate.
The large effect on non-local content is intuitive given that these are the
topics often covered by cable news outlets, so direct borrowing of content by
local newspapers is possible. Finally, column 3 shows the effect on the
textual slant of local news articles, for which direct borrowing is not
possible. There is nonetheless a positive and statistically significant
effect, with point estimates almost identical to our main results.303030Note
that most of the newspaper article snippets are local news (71% are local news
according to human annotation, and 75% according to our topic-model-based
categorization). Therefore, the sample underlying Table 5 is similar to the
one in Table 4.

Thus, slant contagion works on both local and non-local news content. The
effect on local news is notable because it means that cable news influences
the original content of local outlets. An interpretation of these results is
that contagious slant is not working just by reducing the costs of news
production; in addition, local news producers are responding to the shifted
partisan slant preferred by cable-exposed readers, even for local content.
Thus, the results are consistent with demand-side effect of cable news media
slant. See Appendix C.12 for some additional descriptive analysis consistent
with demand side effects.

###  Cable news media slant polarizes local newspapers

Finally, we investigate effect heterogeneity with respect to the prior
partisan commitments of local newspapers. We distinguish three groups of
outlets based on 1996 U.S. presidential election endorsements: (1) those that
endorsed the Republican candidate Bob Dole, (2) those that endorsed the
Democrat candidate Bill Clinton, and (3) those that did not endorse either
candidate.313131In this latter group we include newspapers where we could not
find an explicit endorsement. We think of endorsements as a signal for whether
the pre-FNC/MSNBC political leaning of a newspaper was relatively conservative
or liberal. The non-endorsers can be seen as politically neutral.323232Note
that 1996 endorsements are pre-FNC/MSNBC and so independent of channel
positioning. Our main results are virtually unchanged when including the 1996
endorsement of a newspaper as a control.

Table 6: Cable News Effects on Newspaper Content (2SLS): By Historical
Endorsements

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | -0.463* | 0.062 | 0.259**
| (0.246) | (0.160) | (0.116)
K-P First-Stage F-Stat | 10.586 | 19.708 | 16.070
N observations | 872 | 1858 | 1040
Endorsed Democrat | X |  |
No (Known) Endorsement |  | X |
Endorsed Republican |  |  | X
State FE | X | X | X
Demographic controls | X | X | X
Channel controls | X | X | X
Newspaper language controls | X | X | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC). The right-hand side variable of
interest is instrumented FNC viewership relative to averaged CNN and MSNBC
viewership. In column 1 we only include newspapers that endorsed the
Democratic Presidential candidate in 1996 (pre-FNC era). In column 2, we focus
on newspapers that did not endorse either candidate (or for which endorsement
data is not available). Column 3 considers only newspapers that endorsed the
Republican candidate. All columns include state fixed effects, demographic
controls as listed in Appendix Table A.2, channel controls (population shares
with access to each of the three TV channels), and generic newspaper language
features (vocabulary size, avg. word length, avg. sentence length, avg.
article length). Standard errors are multiway-clustered at the county and at
the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$
0.01.

Table 6 shows the heterogeneity analysis by endorsements, where we re-estimate
our main specification but on different subsets of newspapers. Column 1 limits
the analysis to Democrat-endorsing newspapers, and we find a negative and
significant coefficient, suggesting that newspapers with a more liberal
leaning become less FNC-like in their reporting when more exposed to FNC
(relative to MSNBC and CNN). In column 3, we subset on newspapers that
endorsed the Republican candidate. These newspapers behave the same as the
main sample – a positive coefficient means the more right-wing newspapers
adopt more FNC-like language. Meanwhile, when looking at newspapers with no
endorsements or where no endorsement data is available (column 2), there is no
effect of cable media slant on newspaper content.

These results suggest that cable channel exposure might polarize local news
content, in the sense that right-leaning newspaper outlets follow FNC but move
away from MSNBC/CNN, whereas left-leaning outlets follow MSNBC/CNN while
moving away from FNC. To explore this possibility in more detail, we run an
alternative regression specification that separates out the effects of FNC and
MSNBC/CNN. Specifically, we run reduced-form specifications with
$\text{Slant}_{ijs}$ as the outcome but including two separate treatment
variables: (1) the absolute channel position of FNC and (2) the average
absolute channel positions of CNN and MSNBC.333333Note that the coefficient
signs are reversed in the reduced form specification due to a negative first
stage. A positive coefficient implies that a lower position of a channel leads
to less adoption of slant. We find that the effects measured in Table 6 are
indeed driven by right-leaning newspapers following FNC when it has a lower
channel position and left-leaning newspapers moving away from FNC when it has
a lower channel position. Higher exposure to the liberal channels MSNBC/CNN
has no effect on the content of left-leaning newspapers, while right-leaning
newspapers tend to move away from the liberal channels.343434Appendix Table
C.16 shows the same polarizing trends when looking at Republican vote shares
in the pre-FNC/MSNBC era: It replicates Table 6 but instead of pre-FNC/MSNBC
era newspaper endorsements, distinguishes observations by the county-level
Republican vote share terciles (lowest tercile in the first column, second
tercile in middle, and highest tercile in the last column). Again, the
relative FNC exposure coefficient is negative in the first column, positive
and relatively small in the second column (coefficients in columns 1 and 2 are
not significant), before turning significant, positive, and large in the last
column.

Overall, this evidence suggests that exposure to FNC (and to a lesser degree,
CNN and MSNBC) has polarized local newspaper language. Newspapers with a
right-wing partisan leaning in the pre-FNC/MSNBC era adopt right-wing cable
news language in response to FNC viewership increases. Instead, left-wing
papers become more left-wing. This dynamic could arise from a market
positioning effect [Mullainathan and Shleifer,, 2005, Gentzkow et al.,, 2014].
That is, the conservative papers situate themselves to accommodate FNC-viewer
news preferences. In turn, liberal papers respond and situate to accommodate
non-FNC-viewer news preferences. This type of ideological positioning process
is consistent with demand-side mechanisms.

##  Conclusion

This paper documents that partisan news messaging by large media organizations
can spill over to other news outlets. Specifically, we document that the news
messaging by U.S. cable TV channels influences local news reporting. Regarding
the TV channels, we focus on two poles: on the one hand the Fox News Channel
(which is typically seen as conservative), and, on the other hand CNN and
MSNBC (known to lean liberal). Based on TV show transcripts from these
channels, we build a machine learning model that predicts whether a given
piece of text resembles FNC rather than CNN/MSNBC. We then use this model to
construct a novel measure of partisan slant in local newspapers: the predicted
similarity of their content with FNC versus CNN/MSNBC.

Using exogenous variation in TV channel viewership at the county level, we
show that higher FNC viewership leads to more FNC-like language in local news
– and vice versa for CNN/MSNBC. Furthermore, we build on the fact that the TV
channels address a national audience: We present some evidence that the
newspapers shift the slant in their own local stories, rather than copy-
pasting national or international stories. Moreover, we find that the exposure
to the TV channels leads to a polarization of local news, where outlets that
had already been pro-Republican in the pre-FNC/MSNBC era shift more towards
FNC-like language. In contrast, outlets with a historically pro-Democrat
leaning move towards CNN/MSNBC-like language in response to higher FNC
exposure. These findings add to concerns regarding increasing political
polarization in the U.S. and beyond [e.g., Campbell,, 2018, Carothers and
O’Donohue,, 2019].

These results add to the literature on the political effects of biased news.
We provide new evidence on how partisan media influences not just voting and
policy, but also the content of other media organizations. Our results
highlight that media outlets or technologies cannot necessarily be considered
independent from each other. In addition, future work must allow for secondary
indirect effects of partisan media. Such effects could work on voting and
other outcomes by reshaping news landscapes and influencing the content of
other media providers.

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Online Appendices

##  Appendix A Data Appendix

###  Newspaper articles

First, some more info on NewsLibrary. For each article, the NewsLibrary
provides the newspaper name, the headline, the date, the byline (if any), and
(approximately) the first 80 words of the article.

In principle, NewsLibrary encompasses around 4,000 unique titles for
2005-2008. However, for many titles, there are only a handful of articles
available: around 1,500 titles contain less than 1,000 snippets (for all four
years combined). In all our analyses, we only consider titles with more than
1000 articles. Also, many titles are not local newspapers in the sense that
they cannot be assigned to a county (e.g., the “Army Communicator” or the “Air
& Space” magazine). Furthermore, NewsLibrary often lists different editions of
the same title separately. For instance, “Augusta Chronicle, the (GA)”,
“Augusta Chronicle, the: Web Edition Articles (GA)”, and “Augusta Chronicle,
the: Blogs (GA)” are listed separately. While our initial corpus covers all
2,618 titles with $>$1,000 articles (amounting to almost 50 million article
snippets, see Section A.3), our main analyses focus on 305 titles for which
county-level circulation data is available (see Section 2). We also collapse
different editions of the same outlet (as in the Augusta Chronicle example) to
one observation because the Alliance for Audited Media circulation data is
typically not available separately for different editions of the same title.
The 16 million articles mentioned in the main text refer to the outlets used
in our main regression analyses.

###  Alternative county matching of newspapers

For robustness, we also apply an alternative matching procedure which covers
more newspaper titles, but which only provides total circulation instead of
county-specific circulation. First, we obtain the main county for each
newspaper title based on the newspaper name and geographical information
provided by NewsLibrary (e.g., The Call (Woonsocket, RI) or the Albany
Democrat-Herald (OR)), the U.S. Newspaper Directory, or a manual web search.
For the circulation, we use more broad-based, but less granular data: we
assign total circulation (as of 2004) according to the Inter-University
Consortium for Political and Social Research (ICPSR) to this main county.
Hence, each newspaper is now only assigned to one county, where its total
circulation is assumed to accrue. With this matching approach, we have a
dataset of 682 unique newspaper titles and 24 million article snippets. As
Appendix Table C.7 shows, with the alternative matching, the coefficients are
again statistically significant and around three to four times larger than in
the main Table 4.

###  Filtering and number of article snippets

Table A.1 gives an overview of the number of articles collected and how we
obtain the number of articles used in our main analyses and robustness checks.

Table A.1: Number of Articles Collected and Filtering Filtering | # Articles | Results
---|---|---
No filtering: raw scrapes | 49,891,120 | None (not possible: no
|  | county assignment)
County assignment as in App. A.2 and total | 23,979,516 | Table C.7
circulation data available (ICPSR) |  |
County assignment as in Sec. 2 and county- | 16,098,537 | All other tables
level circulation data available (AAM) |  |

###  Summary statistics

Table A.2: Summary Statistics

Variable | Mean | Std. Dev | Min | Max | N
---|---|---|---|---|---
Newspapers (and newspaper language) |  |  |  |  |
Probability FNC | 0.434 | 0.029 | 0.38 | 0.655 | 3781
Circulation | 5067.311 | 18487.863 | 1 | 390687.8 | 3781
Vocabulary size | 0.026 | 0.023 | 0.007 | 0.146 | 3781
Word length | 7.221 | 0.288 | 6.292 | 8.321 | 3781
Sentence length | 35.846 | 6.826 | 18.858 | 69.036 | 3781
Article length | 447.782 | 112.426 | 193.957 | 918.617 | 3781
# collected articles | 83289.858 | 57642.203 | 1067 | 286027 | 3781
News channels |  |  |  |  |
FNC channel position 2005-2008 | 42.647 | 11.502 | 5 | 74.625 | 3781
CNN channel position 2005-2008 | 30.299 | 10.408 | 1 | 66.306 | 3781
MSNBC channel position 2005-2008 | 45.138 | 13.006 | 4 | 128.5 | 3781
Position (Fox News-0.5(MSNBC+CNN)) | 4.928 | 11.219 | -48.042 | 54.75 | 3781
Position (Fox News - MSNBC) | -2.491 | 13.641 | -81.417 | 55 | 3781
Position (Fox News - CNN) | 12.347 | 12.769 | -39 | 59.618 | 3781
Ratings % Fox News 2005-2008 | 0.539 | 0.354 | 0 | 5.475 | 3781
Ratings % MSNBC 2005-2008 | 0.14 | 0.384 | 0 | 13 | 3781
Ratings % CNN 2005-2008 | 0.303 | 0.229 | 0 | 3.7 | 3781
Ratings (%Fox News - 0.5(%MSNBC - %CNN)) | 0.318 | 0.411 | -7.600 | 5.412 | 3781
Ratings (%Fox News - %MSNBC) | 0.399 | 0.512 | -12.55 | 5.45 | 3781
Ratings (%Fox News - %CNN) | 0.236 | 0.377 | -2.65 | 5.375 | 3781
Share pop. access to FNC | 0.934 | 0.106 | 0.039 | 1 | 3781
Share pop. access to MSNBC | 0.891 | 0.174 | 0.004 | 1 | 3781
Share pop. access to CNN | 0.962 | 0.067 | 0.055 | 1 | 3781
Demographic Controls |  |  |  |  |
Population 2000 | 210572.061 | 509458.494 | 712 | 9818535 | 3781
Republican vote share 1996 | 0.426 | 0.098 | 0.093 | 0.79 | 3781
White | 0.858 | 0.139 | 0.057 | 0.993 | 3781
Black | 0.077 | 0.116 | 0 | 0.795 | 3781
Asian | 0.014 | 0.028 | 0 | 0.359 | 3781
Hispanic | 0.064 | 0.114 | 0.002 | 0.973 | 3781
Male | 0.493 | 0.014 | 0.433 | 0.627 | 3781
Age 10-19 | 0.16 | 0.016 | 0.088 | 0.277 | 3781
Age 20-29 | 0.12 | 0.03 | 0.047 | 0.327 | 3781
Age 30-39 | 0.144 | 0.017 | 0.082 | 0.23 | 3781
Age 40-49 | 0.153 | 0.013 | 0.096 | 0.213 | 3781
Age 50-59 | 0.116 | 0.012 | 0.063 | 0.177 | 3781
Age 60-69 | 0.082 | 0.017 | 0.035 | 0.173 | 3781
Age 70-79 | 0.066 | 0.018 | 0.013 | 0.172 | 3781
Age 80-89 | 0.039 | 0.013 | 0.003 | 0.121 | 3781
Urban | 0.538 | 0.284 | 0 | 1 | 3781
High school | 0.342 | 0.072 | 0.111 | 0.527 | 3781
Some college | 0.263 | 0.049 | 0.108 | 0.424 | 3781
Bachelor | 0.125 | 0.053 | 0.028 | 0.397 | 3781
Postgraduate | 0.068 | 0.04 | 0.013 | 0.31 | 3781
Land area | 309.047 | 405.331 | 1.768 | 6812.404 | 3781
Population density | 4.454 | 1.706 | -1.08 | 10.307 | 3781
Mean log. income | 10.794 | 0.231 | 10.105 | 11.597 | 3781
Gini index | 0.429 | 0.036 | 0.335 | 0.604 | 3781
Occ. management and professional | 29.63 | 6.791 | 16.6 | 61.3 | 3781
Occ. service | 15.466 | 2.809 | 8.1 | 31.9 | 3781
Occ. sales and office | 24.449 | 2.978 | 13.6 | 32.6 | 3781
Occ. farming, fishing, and forestry | 1.43 | 1.722 | 0 | 24.9 | 3781
Occ. construction, extraction, and maintenance | 10.818 | 2.644 | 2.3 | 24.5 | 3781

##  Appendix B Methods Appendix

Here, we present additional material related to the measurement of cable news
slant.

###  Bigrams most predictive for FNC or CNN/MSNBC

Table B.1 lists the 200 bigrams that are most predictive for a transcript
being from FNC or CNN/MSNBC, respectively (specifically, the bigrams that come
with the largest absolute coefficients in the logistic regression from Section
3).

Table B.1: Top 200 of bigrams predictive for FNC or CNN/MSNBC transcripts

FNC-Related |  | MSNBC/CNN-Related
---|---|---
segment tonight | steve thank |  | situat room | point tonight
fox news | thank sean |  | anderson cooper | stay welcom
correspond mike | welcom washington |  | king live | report baghdad
final tonight | join reaction |  | senior polit | later today
dick morri | come straight |  | person world | live baghdad
mr oreilli | ahead continu |  | live pictur | polit analyst
chief white | jim thank |  | best polit | stay stori
power player | fair balanc |  | great appreci | york good
david lee | doubl homicid |  | watch cnn | thank ahead
correspond jim | got run |  | david shuster | food drug
come panel | whos stand |  | cnn report | hes author
sean know | wrap thing |  | hello hello | im join
sean hanniti | georg soro |  | glenn beck | mari thank
jame thank | laura ingraham |  | wolf blitzer | real stori
al qaida | record right |  | jack thank | eastern right
roll tape | deepak satish |  | join tomorrow | later broadcast
plenti ahead | time left |  | question hour | let ahead
live vote | jonathan thank |  | chief medic | washington thank
went record | captur terrorist |  | thank larri | york stock
join author | karen hanretti |  | share stori | morn thank
latest polit | casey anthoni |  | david gergen | day presidenti
jennif thank | chief polit |  | stori work | quick want
homicid detect | lack better |  | polit news | pat buchanan
right colonel | join boston |  | noon eastern | andrew speaker
bring legal | minut left |  | hello yes | news cnn
brit hume | war stori |  | im toni | thank lot
later special | play tape |  | hes join | tonight real
secular progress | later rememb |  | watch stori | new concern
let screen | right mr |  | work stori | bit earlier
anyth unusu | ted know |  | donna brazil | investig unit
headlin come | illeg right |  | dont away | sure appreci
ahead welcom | dr michael |  | paul begala | right susan
senat mcconnel | report moment |  | tonight eastern | thank joe
ill word | michell malkin |  | ramo compean | want updat
emin domain | senior command |  | know larri | come situat
live phone | sort thought |  | thank ed | andrea mitchel
panel stay | regular folk |  | live today | result poll
mr speaker | drill anwr |  | spi program | thank toni
thank panel | anywher world |  | eastern pacif | hello everyon
kiryat shmona | happen join |  | headlin news | ahead tonight
griffin report | michael reagan |  | weather center | heart diseas
steve forb | right sir |  | news debat | happen news
thank major | thank special |  | david gregori | youll want
mike thank | forens pathologist |  | pentagon correspond | news follow
second left | tell later |  | look headlin | new pictur
far left | bring panel |  | pleas join | talk morn
join los | stay im |  | shes join | closer look
record good | look come |  | stay come | ill speak
oclock morn | fair doctrin |  | good everybodi | bailout plan

FNC-Related |  | MSNBC/CNN-Related
---|---|---
oh stop | crimin alien |  | danc star | stori day
stori wont | search underway |  | stori stay | hour presid
later program | yes polic |  | impact world | dow point
thank moment | marriott hotel |  | bring date | race white
note earlier | left wing |  | littl ago | transport safeti
record come | special report |  | ill talk | job lost
amber frey | right panel |  | health offici | correspond dana
join dalla | dr dobson |  | winter storm | earlier morn
holiday inn | youll meet |  | watch unfold | join talk
governor good | light fact |  | larri king | join tonight
welcom good | homicid investig |  | lung cancer | atlanta georgia
strong economi | upper incom |  | hurrican ike | work men
da mike | live scene |  | hous thank | vinci code
welcom program | killer killer |  | complet coverag | steve fossett
charl krauthamm | welcom aboard |  | right ed | stori follow
undermin presid | hezbollah hama |  | listen senat | receiv copi
right sean | murder scene |  | tom cruis | mind busi
appreci guy | franklin graham |  | come hour | continu watch
come continu | atm card |  | great thank | talk tomorrow
news correspond | later polic |  | develop news | littl clip
public radio | jim know |  | thank time | im chris
sneak peek | media research |  | associ editor | short break
dr perper | captain thank |  | war room | john dean
code pink | time pleas |  | let listen | bird flu
drew peterson | john kelli |  | good morn | right larri
guilt associ | headlin new |  | talk live | begin tonight
welcom come | senat schumer |  | pictur come | lot join
legitim point | westchest counti |  | new studi | look stori
brett favr | liber want |  | larri thank | hold news
correspond jeff | madison wisconsin |  | miss soldier | thank updat
execut editor | abl determin |  | toni know | jone industri
live aruba | black panther |  | report thank | day news
ward churchil | aruban polic |  | polit director | weve hear
elect decemb | scott peterson |  | poll tonight | drug cartel
dispos bodi | join san |  | thank david | shes watch
mark thank | inform tonight |  | senior editor | chris matthew
gloria allr | doctor thank |  | meantim let | let stori
ladi thank | congressman good |  | thank report | live white
sir come | arm control |  | new number | end video
chief thank | frantic search |  | day number | poll believ
privat jet | judg instruct |  | big board | look thought
father pfleger | american media |  | sever weather | snow new
loui farrakhan | murder right |  | valeri wilson | thank want
beth holloway | duke case |  | let head | war middl
ninth circuit | gaza strip |  | flight cancel | hi everybodi
senat graham | chief prosecutor |  | expect hear | thank brian
ad quot | interview fox |  | american idol | big number
respect law | rais good |  | let play | winner loser
guest say | byron york |  | harri potter | report pentagon

###  Human validation of NLP model

We evaluate the accuracy of human guesses on whether an 80-word TV transcript
snippet is from FNC or CNN/MSNBC. This section provides some more detail on
this validation step. We extract a random sample of 1,000 TV transcript
snippets and ask three individual freelancers to guess whether each snippet is
from FNC or CNN/MSNBC.

The individuals were recruited from the freelancing platform Upwork. When
selecting the individuals, we imposed these three filtering criteria: must (i)
live in the United States, (ii) be socialized in the U.S. (e.g., born and
raised in the U.S.), and (iii) show good literacy (defined by properly reading
our instructions, i.e., returning a valid working sample, see below). The
initial job post read as follows: “We have a file with 1,000 very short
excerpts of news reports. You will read them and spontaneously (based on your
intuition) decide if you think a given excerpt was published by Fox News or by
CNN. In the process of labelling, do not google or engage in any other form of
research. Just give us your spontaneous impression based on how you perceive
news reporting by the two channels in your everyday life.” All freelancers who
replied to this post within a day were requested to submit a working sample of
10 snippets. We recruited the first three individuals who submitted the
requested working sample. The hired freelancers received the file with the
reminder: “Please indicate whether you think the text is from Fox News or CNN.
We would like to remind you that you mustn’t do research of any kind when
assessing the excerpts. Your labels should be based on your spontaneous guess
and nothing else.” All individuals had or were in the process of acquiring a
college degree. They were based in Point Pleasant (WV), Malvern (PA), and
Houston (TX).

The accuracy scores of the freelancer’s guesses are 0.73, 0.78, and 0.78,
respectively. The average false-positive rate (a freelancer guesses a
CNN/MSNBC snippet to be from FNC) is slightly higher (at 0.14) than the false-
negative rate (0.08). The three freelancers agree on whether a snippet appears
to be from FNC or from CNN/MSNBC in 58% of cases (if they guessed randomly,
they would agree in 25% of cases). We derive two conclusions from this
exercise. First, even if cut into 80-word snippets, TV transcripts still
contain information that allows a reader to infer the channel. Second, our
classifier approximates the performance of humans.

###  Distribution of Fox News similarity in newspapers

Figure B.1: Distribution of Local Newspaper Content Similarity with FNC

_Notes:_ Histogram (bin width 0.005) of newspaper-level predictions. The
figure shows the absolute frequency (unique newspaper counts) against the
average value of FNC similarity by newspaper ($\widehat{FNC}_{j}$). For most
newspapers, we predict that – on average – a snippet resembles FNC with a
probability between 0.40 and 0.45.

###  Example articles by Fox News similarity

Table B.2: Newspaper articles that are most similar to Fox News Channel shows

The Free Lance-Star (Fredericksburg, VA), 2 January 2008
98% similarity to FNC

Regarding their recent op-ed ["The Pentagon should stay out of Africa," Dec.
14], I am afraid Danny Glover and Nicole Lee are victims of misinformation
about U.S. Africa Command. AFRICOM is not part of a "U.S. military expansion,"
nor will it involve placing many "American troops on foreign soil." Rather,
AFRICOM marks recognition of the growing importance of Africa and reallocates
responsibility for U.S. security interests accordingly. The U.S. Department of
Defense assigns […]

The Sacramento Bee (CA), 19 May 2007
97% similarity to FNC

Don Kercell thinks he’s earned a second chance. The Contractors State License
Board does not agree. And therein lies a tale of choices and consequences;
crime and punishment; addiction and rehabilitation; public protection and
personal redemption – and second chances. Kercell is a 48-year-old resident of
Rio Linda. In his youth, he discovered two things. One was that he had a
talent for working with concrete. The other was methamphetamine. […]

Joplin Globe, the (MO), 28 April 2007
95% similarity to FNC

Bush vows to veto any attempts by Dems to force troop pullout CAMP DAVID, Md.
– President Bush warned Congress Friday that he will continue vetoing war
spending bills as long as they contain a timetable for the withdrawal of
American troops from Iraq. Speaking a day after the Democratic-controlled
Congress approved legislation that requires that a troop drawdown begin by
Oct. 1, Bush said – as he has before - he will veto it because of that demand.
He […]

The Commercial Appeal (Memphis, TN), 16 June 2008
83% similarity to FNC

WASHINGTON – One momentous case down, another equally historic decision to go.
The Supreme Court returns to the bench Monday with 17 cases still unresolved,
including its first-ever comprehensive look at the Second Amendment’s right to
bear arms. The guns case – including Washington, D.C.’s ban on handguns – is
widely expected to be a victory for supporters of gun rights. Top officials of
a national gun control organization said this week that they expect the
handgun ban to be […]

Table B.3: Newspaper articles that are most similar to CNN and MSNBC shows

The Sun (San Bernardino, CA), 21 March 2005
3% similarity to FNC

REDLANDS - A week after a state judge ruled that banning gay marriage is
unconstitutional, students at University of Redlands will celebrate the
milestone along with continued efforts to raise awareness of the gay
community.The PRIDE Alliance, a campus group devoted to promoting tolerance on
campus for gay, lesbian, bisexual and transgender students, will celebrate
PRIDE Week at the university through Friday. A series of events is scheduled
to raise awareness on campus and in the […]

Robesonian, the (Lumberton, NC), 12 October 2007
4% similarity to FNC

About $18 billion a year has been drained from Africa by nearly two dozen wars
in recent decades, a new report states, a price some officials say could’ve
helped solve the AIDS crisis and created stronger economies in the world’s
poorest region."This is money Africa can ill afford to lose," Liberian
President Ellen Johnson Sirleaf wrote in an introduction to the report by the
British charity Oxfam and two groups that seek tougher controls on small arms,
Saferworld […]

Denver Examiner (CO), 26 September 2008
5% similarity to FNC

John McCain and Barack Obama will indeed debate tonight at 7 p.m at the
University of Mississippi, moderated by Jim Lehrer. The debate is scheduled to
focus on issues of foreign policy, but given the economic meltdown of the last
two weeks, and the Bush administration’s proposed $700 billion bailout plan,
Politico is reporting that Lehrer might add in some questions on the economy.
Also, Rich Lowry from National Review is reporting that everyone at Ole Miss
"hates" McCain for […]

Long Beach Press-Telegram (CA), 22 June 2006
6% similarity to FNC

There is finally some good news about the most sinister drug on the black
market: crystal methamphetamine. Nationwide demand and production is down,
according to federal drug cops. Meth, which has been linked to the spread of
HIV in Long Beach’s gay community, is still out there, but law enforcement
officials say plenty of busts are reducing supplies. We hope that treatment is
part of the equation nationwide as it is California, where voters agreed to
put more users in treatment than in […]

###  Topics from the transcript-based LDA model

Table B.4: Transcript-Based LDA Topic Model: List of 128 Topics

Most frequent tokens | Topic label
---|---
victim destroy katrina crowd relief | disaster
station ride regular stern dinner | infrastructure
know mean like thing dont | opinion
worst scenario chavez bowl cuba | communism
governor state california mexico alert | state governments
year ago old jail time | crime
car sunni shia militia say | islam
im know right say ok | no label
tape context say pop sat | scandals
key saudi stronger arabia plame | foreign intelligence service
shouldnt object option greatest fault | no label
arizona missouri wisconsin ice home | us states
deal ship port big built | ports and nautics
money spend want make know | money
girl young boy yearold sexual | child protection
god pain plant bless exist | god and christian religion
ahead date pope straight new | god and christian religion
father son daughter dna super | family
hezbollah profit rocket quarter rapid | terrorism
human exit right male gap | internet
life heart lay react rid | lifestyle and health
pakistan british london britain musharraf | international politics
right look eye blood like | health and accidents
shes woman mother mom know | motherhood
progress union dream boat equal | unions and labor
group air citizen learn defend | no label
iran nuclear weapon iranian say | international politics
confirm wilson memo hide mr | international politics
travel bus gate seat broken | travel
school student teacher high class | schools
alan drive driver detect contract | economy
africa know deepak tj stadium | international politics
nice rumsfeld donald trump stone | defense
killer rock birth pro scientist | crime
lawyer prosecutor say attorney appeal | judiciary
palestinian meet ban item gaza | middle east
war iraq world terror american | military and war
faith duke jesus shame christ | god and christian religion
miss search suspect disappear aruba | crime

  * 1.

Notes: The 128 topics from the transcript-based model and their labels. The
first columns shows the most frequent tokens for each topic. The second column
lists the manually chosen topic labels. Sometimes, two or more topics are
similar and receive the same label. For 28 out of 128 topics, no obvious label
emerges. Table continued on the next page.

Most frequent tokens | Topic label
---|---
children die husband wife space | family
social account gore benefit invest | social insurance
truth pardon fourth alabama campus | judiciary
edward lieberman colorado connecticut neck | crime
thank come right join sir | events
vote voter democrat state republican | democracy and voting
pay billion dollar cost tax | economy
protest research stem complex cell | protests
reverend embarrass pastor speech sharpton | god and christian religion
white say africanamerican staff bush | racial issues
elect june day year turnout | democracy and voting
afghanistan chief withdraw tactic troop | military and war
hi stage hello studio hollywood | no label
interview politician talk hair say | politics
palin sarah japan alaska yard | tea party
fish steroid cabinet say youth | no label
word nbc news page right | no label
wear cloth dress shirt look | lifestyle and health
price gas higher fuel oil | oil and oil products
murder attorney death general professor | crime
appoint voter veto anger know | politics
percent care health say number | healthcare
ph glad sergeant corp art | military and war
million cut fund store budget | economy
inform threat warrant collect time | no label
mayor guard corrupt new nation | politics
shoot newsroom live atlanta da | no label
terrorist qaeda attack terror bin | terrorism
kid book parent right know | family
radio leadership republican talk host | politics
north south carolina korea korean | no label
saw bomb train scene film | no label
visit honor flag sight winner | honoring america
game play saddam san trial | no label
storm west coast wind florida | weather
kick ring scream jew fat | crime
bush prime rate mr say | international politics
closer shelter right juror louisiana | local politics
child avail ticket patient various | no label
prison sex offend colonel rice | imprisonment
plane angel los airport crash | aviation
sponsor pitch inject bat flipflop | baseball
yes dont know right like | no label
ive phone video know got | no label

  * 1.

Notes: The 128 topics from the transcript-based model and their labels. The
first columns shows the most frequent tokens for each topic. The second column
lists the manually chosen topic labels. Sometimes, two or more topics are
similar and receive the same label. For 28 out of 128 topics, no obvious label
emerges. Table continued on the next page.

Most frequent tokens | Topic label
---|---
democrat republican clinton mccain campaign | national politics
site web internet post email | internet
dog mass smoke cold thing | no label
women men speaker pelosi indiana | politics
reagan accept gay director ronald | national politics
state unit iraq govern iraqi | international politics
door georgia sheriff bear born | no label
vice cheney dick red jersey | national politics
law freedom religion protect right | freedom
hate brain coulter lunch compel | hate
gun lie sound small like | guns and shooting
job china buy american product | economy
confess column repair trick dispatch | celebrity news
east lebanon syria camp hezbollah | international politics
hes romney perform mitt know | no label
question border answer ask agent | border control
status abc brutal sold ton | no label
court rule record legal standard | judiciary
obama barack mccain clinton campaign | national elections
said say know didnt talk | no label
congress worker program member guest | politics
iraq troop iraqi soldier kill | military and war
home friend like feel felt | home and neighbours
credit bank card oj simpson | economy
fight forth distinct loud shoulder | no label
fbi strike depart homeland new | homeland security
media list theyll know cop | media
forward crime commit intent say | crime
push trail planet sooner right | no label
need joran drill sloot bail | no label
land occur board flight wing | aviation
cancer treatment english brand aint | healthcare
tax cut tire talk hes | taxation
ad doctor dr say treat | healthcare
grand schiavo pray walter feed | no label
tonight poll new tomorrow news | no label
oil barrel oprah nifong say | oil and oil products
think dont know want thing | no label
hour player ohio box alcohol | no label
test special sunday eastern come | no label
area weather flood rain island | weather
drug letter say use tool | drugs
amend control stake cbs award | no label
street market wall stock apart | infrastructure
food rush model tree limbaugh | agriculture

  * 1.

Notes: The 128 topics from the transcript-based model and their labels. The
first columns shows the most frequent tokens for each topic. The second column
lists the manually chosen topic labels. Sometimes, two or more topics are
similar and receive the same label. For 28 out of 128 topics, no obvious label
emerges. Table continued on the next page.

###  Topics from the newspaper-based LDA model

Table B.5: Newspaper-Based LDA Topic Model: List of 128 Topics

Most frequent tokens | Topic label | Local news
---|---|---
davi broker morgan stanley princeton | international economic actors | 0
plant garden winter farmer flower | farmers | 1
dairi payn lilli liabil utica | no label | 0
probat fine suspend penalti ppg | crime | 1
collin prayer omaha floyd billi | small city names | 1
ice indiana chip hall fame | food | 1
light marshal lane bennett home | local happenings | 1
law immigr illeg enforc clinton | border control | 0
harrison intellig counsel harper island | no label | 0
park water lake land river | nature and infrastructure | 1
springfield indian martinez tribe riley | local happenings | 1
paul pope novel decatur roman | names | 1
health care medic hospit center | healthcare | 1
airport said nuclear plane iran | aviation and terrorism | 0
oregon wine meter sullivan wildlif | nature and infrastructure | 1
rate credit class flag glass | economy | 0
secur report exchang act date | no label | 0
offic agenc number address post | post | 1
navi laker naval salina reagan | no label | 0
walker nevada dear easter mother | family | 1
bird boulder rent rumor wagner | farmers | 1
weather snow temperatur day degr | weather | 1
club golf channel cour rain | sports and associations | 1
ford busi small negoti sewer | no label | 0
new announc cancer technolog program | technology | 0
phillip perri campbel crawford year | names | 1
right left disabl miller list | healthcare | 1
current estim ratio consensus low | international economic actors | 0
school high student graduat colleg | education | 1
art museum artist paint exhibit | arts and culture | 1
drink mental said jacksonvil sleep | alcoholism | 1
pound donat blood baker weigh | charities | 1
harbor sioux alt nichol wheeler | no label | 0
concert israel orchestra drill isra | no label | 0
compani million inc financ bank | economy | 0
like time year get day | no label | 0
salem african sex violenc offend | crime (international) | 0
event annual celebr saturday award | holidays | 1
daytona ranger wyom walsh bengal | no label | 0

  * 1.

Notes: The 128 topics and their labels. The first columns shows the most
frequent tokens for each topic. The second column lists the manually chosen
topic labels. Sometimes, two or more topics are similar and receive the same
label. For 22 out of 128 topics, no obvious label emerges. The last column
captures whether we label the topic to be indicative of local rather than non-
local (national, international) news. Table continued on the next page.

Most frequent tokens | Topic label | Local label
---|---|---
recycl mitchel firework merchant berlin | no label | 0
girl boy basketb team tournament | sports and associations | 1
iowa coloni fork delta cousin | no label | 0
hurrican storm orlean katrina scout | disaster | 1
polic said man offic report | crime | 1
church servic fort wayn king | religion | 1
beach bond sacramento barri borough | local infrastructure | 1
davenport coleman consolid newark freeman | small city names | 1
cross chapter heart royal kati | no label | 0
rapid cedar huntington myer dispatch | no label | 0
pierc warner celtic augustin year | local services | 1
news page editor letter mail | media | 1
jewish griffin supplement year penguin | nature | 1
coach team season footbal game | sports and associations | 1
ring hopkin waterloo peanut philippin | no label | 0
santa idaho mph wind powel | weather | 1
fayettevil newport rhp montreal alarm | small city names | 1
holland cole bedford missionari zion | local happenings | 1
food chicken appl fresh recip | food | 1
ashland mapl boyd ash birmingham | small city names | 1
film movi inch screen jone | television | 0
die home funer born son | family | 1
book children child read parent | family | 1
stock share trade type date | economy | 0
palm charg kent beach counti | crime | 1
estim consensus buy hold sell | economy | 0
govern pari lebanon attack said | international policitics | 0
manag new build applic develop | economy | 0
los que del las por | spanish word parts | 0
san final chicago sport new | no label | 0
serv marin vega utah bend | military | 0
team game first state win | sports and associations | 1
meet counti center inform call | local events | 1
card jackson comput ident check | assets | 0
point score run game win | sports and associations | 1
inmat jail aberdeen blind counti | no label | 0
estim current previous next report | economy | 0
ridg oak cape fli flint | local surroundings | 1
danc fish swim lesson pool | leisure | 1
island rhode jacob hanov hilton | no label | 0
circl rod roof turkey fbi | no label | 0
architect pipe middletown year benson | local infrastructure | 1
world global ship warm africa | climate change | 0
bridg traffic truck transport interst | traffic and infrastructure | 1

  * 1.

Notes: The 128 topics and their labels. The first columns shows the most
frequent tokens for each topic. The second column lists the manually chosen
topic labels. Sometimes, two or more topics are similar and receive the same
label. For 22 out of 128 topics, no obvious label emerges. The last column
captures whether we label the topic to be indicative of local rather than non-
local (national, international) news. Table continued on the next page.

Most frequent tokens | Topic label | Local label
---|---|---
shaw chili year resolv aug | no label | 0
state bill hous tax feder | national politics | 0
anderson wichita granddaught victoria alexand | names and family | 1
windsor bow elgin perkin sanford | no label | 0
call phone confer mine press | international policitics | 0
nelson horn wore said ordinari | local happenings | 1
kansa oakland smith sport network | small city names | 1
monro sept col nobl camden | no label | 0
citi board council counti plan | local politics | 1
year baseb new like first | sports and associations | 1
drove said furnitur gun mckinney | crime | 1
site com www web onlin | internet | 0
bush muslim bee year abort | national politics | 0
anim dog human pet cat | pets | 1
railroad riversid rail jul fairfield | infrastructure | 0
real estat new jersey coff | real estate | 1
mar space broadcast alert maryland | space and technology | 0
mobil breakfast hair lopez rutger | local services | 1
minist china said prime foreign | international policitics | 0
tree cup egg salt sugar | food | 1
percent year increa counti rate | economy | 0
store shop question answer box | shopping | 1
bull get think time want | no label | 0
drug librari possess marijuana dec | drugs | 1
hole shot round par dame | sports and associations | 1
eve watson woodland wade poster | local infrastructure | 1
price year gas oil get | oil and oil products | 0
race vote elect voter ballot | local politics | 1
music show perform band theater | arts and culture | 1
mart wal beer penn zoo | local services | 1
alabama casino portland auburn memphi | local infrastructure | 1
fire firefight depart burn said | fire and firefighters | 1
court judg charg counti attorney | crime | 1
ticket gay arlington gordon victor | local services | 1
wilson wrestl olymp stewart warren | sports and associations | 1
peterson aurora spur hancock dawson | local services | 1
youth camp day peoria summer | local events | 1
supervisor petit counti signatur cumberland | local politics | 1
farm syracus intersect road paso | small city names | 1
restaur cook smoke food eat | food | 1
war iraq militari veteran armi | military and war | 0
peopl get map mani exerci | daily life | 1
consum montana store destin fare | shopping | 1
ride year hor old dad | local services | 1
presid democrat republican elect state | national politics | 0

  * 1.

Notes: The 128 topics and their labels. The first columns shows the most
frequent tokens for each topic. The second column lists the manually chosen
topic labels. Sometimes, two or more topics are similar and receive the same
label. For 22 out of 128 topics, no obvious label emerges. The last column
captures whether we label the topic to be indicative of local rather than non-
local (national, international) news.

##  Appendix C Results Appendix

###  First stage results

Table C.1: Cable TV Position Effects on Viewership (First Stage)

Dep. variable: FNC Viewershipijs | (1) | (2) | (3)
---|---|---|---
(rel. to CNN/MSNBC) |  |  |
FNC Position (rel. to CNN/MSNBC) | -0.113*** | -0.113*** | -0.114***
| (0.019) | (0.019) | (0.020)
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: First stage estimates. Cross-section with newspaper-county-level
observations weighted by newspaper circulation in each county. The dependent
variable is FNC viewership (relative to averaged CNN and MSNBC viewership).
The right-hand side variable of interest is the channel position of FNC,
relative to the averaged position of CNN and MSNBC viewership. All columns
include state fixed effects and demographic controls as listed in Appendix
Table A.2. Column 2 also includes channel controls (population shares with
access to each of the three TV channels). Column 3 controls for generic
newspaper language features (vocabulary size, avg. word length, avg. sentence
length, avg. article length). Standard errors are multiway-clustered at the
county and at the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$
0.05, *** p $<$ 0.01.

###  Placebo: Content Similarity in 1995/96

Table C.2: Placebo Cable News Effects on Newspaper Content (2SLS)

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | -0.133 | -0.078 | -0.302
| (0.456) | (0.424) | (0.714)
K-P First-Stage F-stat | 18.265 | 18.276 | 13.654
N observations | 1143 | 1143 | 1143
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC) in 1995/1996 (pre-FNC era). The
text similarity scores use the 2005-2008 TV transcripts (same as the main
analysis) because FNC and MSNBC did not yet exist in 1995-1996. The right-hand
side variable of interest is instrumented FNC viewership relative to averaged
CNN and MSNBC viewership. All columns include state fixed effects and
demographic controls as listed in Appendix Table A.2. Column 2 also includes
channel controls (population shares with access to each of the three TV
channels). Column 3 controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  OLS results

Table C.3 shows OLS results for regressions of predicted Fox News similarity
for newspaper $ijs$, $\text{Slant}_{ijs}$, on TV channel viewership. Column 1
looks at FNC viewership relative to averaged MSNBC and CNN viewership. It
hence shows the OLS estimates that mirror the 2SLS results in the main Table 4
(specifically, column 3). In columns 2 and 3, we look at FNC viewership
relative to CNN and MSNBC separately. Column 4 focuses on absolute FNC
viewership. All columns include state fixed effects and demographic controls
as listed in Appendix Table A.2, channel controls (population shares with
access to each of the three TV channels), and controls for generic newspaper
language features (vocabulary size, avg. word length, avg. sentence length,
avg. article length). The OLS coefficients are positive, as the 2SLS
coefficients, though smaller in magnitude and only significant for absolute
FNC viewership.

Table C.3: Cable News Effects on Newspaper Content (OLS)

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3) | (4)
---|---|---|---|---
FNC Viewership (rel. to CNN and MSNBC) | 0.015 |  |  |
| (0.011) |  |  |
FNC Viewership (rel. to CNN) |  | 0.019 |  |
|  | (0.014) |  |
FNC Viewership (rel. to MSNBC) |  |  | 0.012 |
|  |  | (0.010) |
FNC Viewership (absolute) |  |  |  | 0.028**
|  |  |  | (0.013)
N observations | 3781 | 3781 | 3781 | 3781
State FE | X | X | X | X
Demographic controls | X | X | X | X
Channel controls | X | X | X | X
Newspaper language controls | X | X | X | X

  * 1.

Notes: OLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC):
$\widehat{FNC}_{jik}$=$P(FNC|Text_{jik})$. In the first column, the right-hand
side variable of interest is FNC viewership relative to averaged CNN and MSNBC
viewership. In the second column, it is FNC viewership relative to CNN
viewership. In the third, it is FNC viewership relative to MSNBC viewership.
Finally, in the fourth column, it is absolute FNC viewership. All columns
include state fixed effects and demographic controls as listed in Appendix
Table A.2, channel controls (population shares with access to each of the
three TV channels), and controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Reduced form results

Table C.4: Cable News Effects on Newspaper Content (Reduced Form)

Dep. variable: Slantijs | (1) | (2) | (3)
---|---|---|---
FNC Position (rel. to CNN/MSNBC) | -0.036*** | -0.035*** | -0.036***
| (0.012) | (0.012) | (0.013)
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: Reduced form estimates. Cross-section with newspaper-county-level
observations weighted by newspaper circulation in each county. The dependent
variable is newspaper language similarity with FNC (the average probability
that a snippet from a newspaper is predicted to be from FNC). The right-hand
side variable of interest is the channel position of FNC, relative to the
averaged position of CNN and MSNBC viewership: Position (FNC - 0.5(MSNBC -
CNN))). All columns include state fixed effects and demographic controls as
listed in Appendix Table A.2. Column 2 also includes channel controls
(population shares with access to each of the three TV channels). Column 3
controls for generic newspaper language features (vocabulary size, avg. word
length, avg. sentence length, avg. article length). Standard errors are
multiway-clustered at the county and at the newspaper level (in parenthesis):
* p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Sub-samples: Newspaper Headquarters and Other Counties

Table C.5: Cable News Effect on Newspaper Content (2SLS): Newspaper-
Headquarter Counties

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.711** | 0.701** | 0.684**
| (0.288) | (0.287) | (0.287)
K-P First-Stage F-Stat | 11.630 | 11.659 | 11.982
N observations | 263 | 263 | 263
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. This Table only includes
newspaper-county observations where the county coincides with the newspaper
headquarters. The dependent variable is newspaper language similarity with FNC
(the average probability that a snippet from a newspaper is predicted to be
from FNC). The right-hand side variable of interest is instrumented FNC
viewership relative to averaged CNN and MSNBC viewership. All columns include
state fixed effects and demographic controls as listed in Appendix Table A.2.
Column 2 also includes channel controls (population shares with access to each
of the three TV channels). Column 3 controls for generic newspaper language
features (vocabulary size, avg. word length, avg. sentence length, avg.
article length). Standard errors are multiway-clustered at the county and at
the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$
0.01.

Table C.6: Cable News Effect on Newspaper Content (2SLS): Non-Newspaper-
Headquarter Counties

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.132 | 0.127 | 0.109
| (0.081) | (0.079) | (0.106)
K-P First-Stage F-Stat | 47.698 | 49.911 | 46.439
N observations | 3507 | 3507 | 3507
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. This Table only includes
newspaper-county observations where the county does not coincide with the
newspaper headquarters. The dependent variable is newspaper language
similarity with FNC (the average probability that a snippet from a newspaper
is predicted to be from FNC). The right-hand side variable of interest is
instrumented FNC viewership relative to averaged CNN and MSNBC viewership. All
columns include state fixed effects and demographic controls as listed in
Appendix Table A.2. Column 2 also includes channel controls (population shares
with access to each of the three TV channels). Column 3 controls for generic
newspaper language features (vocabulary size, avg. word length, avg. sentence
length, avg. article length). Standard errors are multiway-clustered at the
county and at the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$
0.05, *** p $<$ 0.01.

###  Robustness: Alternative county matching

Table C.7: Cable News Effects on Newspaper Content (2SLS): Alternative
Matching Procedure

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.859** | 1.051** | 1.164**
| (0.338) | (0.426) | (0.455)
K-P First-Stage F-Stat | 19.704 | 14.828 | 13.869
N observations | 682 | 682 | 682
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X