# Evidence of Demographic rather than Ideological Segregation
in News Discussion on Reddit

Corrado Monti<EMAIL_ADDRESS>0000-0001-6846-5718 CENTAICorso
Inghilterra 3TurinItaly10138 ,  Jacopo D’Ignazi<EMAIL_ADDRESS>0000-0003-2843-5279 ISI FoundationVia Chisola 5TurinItaly10126 ,  Michele
Starnini<EMAIL_ADDRESS>0000-0002-9161-5339 CENTAICorso
Inghilterra 3TurinItaly10138 and  Gianmarco De Francisci Morales<EMAIL_ADDRESS>0000-0002-2415-494X CENTAICorso Inghilterra 3TurinItaly10138

(2023)

###### Abstract.

We evaluate homophily and heterophily among ideological and demographic groups
in a typical opinion formation context: online discussions of current news. We
analyze user interactions across five years in the r/news community on Reddit,
one of the most visited websites in the United States. Then, we estimate
demographic and ideological attributes of these users. Thanks to a comparison
with a carefully-crafted network null model, we establish which pairs of
attributes foster interactions and which ones inhibit them.

Individuals prefer to engage with the opposite ideological side, which
contradicts the echo chamber narrative. Instead, demographic groups are
homophilic, as individuals tend to interact within their own group—even in an
online setting where such attributes are not directly observable. In
particular, we observe age and income segregation consistently across years:
users tend to avoid interactions when belonging to different groups. These
results persist after controlling for the degree of interest by each
demographic group in different news topics. Our findings align with the theory
that affective polarization—the difficulty in socializing across political
boundaries—is more connected with an increasingly divided society, rather than
ideological echo chambers on social media. We publicly release our anonymized
data set and all the code to reproduce our
results.111https://github.com/corradomonti/demographic-homophily

Homophily, Socio-demographic factors, Polarization, News, Reddit

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
ACM Web Conference 2023; May 1–5, 2023; Austin, TX, USA††booktitle:
Proceedings of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin,
TX, USA††price: 15.00††doi: 10.1145/3543507.3583468††isbn:
978-1-4503-9416-1/23/04††ccs: Information systems World Wide Web††ccs: Applied
computing Sociology††ccs: Human-centered computing Social networking sites

##  1\. Introduction

Socializing across ideological boundaries has been reported to be increasingly
difficult (Iyengar et al., 2019). This phenomenon, known as _affective
polarization_ , has been widely observed in the United States. For instance,
the fraction of Republicans and Democrats that would be unhappy if their child
married a supporter of the opposing party has increased from 5% in 1960, to
more than 30% in 2010 (Iyengar et al., 2012). This kind of polarization
contributes to creating a harsh political climate, where citizens believe that
other people’s opinions are irrational and not based on facts (Jackson, 2018).
It has also been associated with the spread of harmful conspiracy theories,
which can lead to dehumanization (Harel et al., 2020), culminating in threats
such as the emergence of far-right domestic terrorism (Lowery et al., 2018;
Michael, 2017).

The causes of affective polarization are widely debated, and some studies have
pointed at social media as a decisive factor (Garrett, 2009). In this popular
view (Pariser, 2011), social media—through recommendation algorithms,
algorithmic filter bubbles, and self-sorting—would reinforce ideological
separation, thus making it harder for an individual to engage with information
from the opposite side. While several studies have observed ideological
homophily and echo chambers in some social media (Conover et al., 2011;
Garimella et al., 2018; Cota et al., 2019; Cinelli et al., 2021), in others
interactions between opposing sides are the norm and not the exception (Dubois
and Blank, 2018; De Francisci Morales et al., 2021; Treen et al., 2022;
Cinelli et al., 2021). Some scholars have argued that online media has
actually increased chances of engaging with the opposite ideological side
(Fletcher and Nielsen, 2018), and Internet usage explains only a very small
share of polarization (Boxell et al., 2017).

An alternative explanation for the increase in affective polarization lies in
the role of demographic factors (Mason and Wronski, 2018; Bradley and
Chauchard, 2022; Phillips, 2022). Underlying material rifts and inequalities
in society make gender, race, age, and wealth boundaries increasingly
divisive, and also increasingly correlated to party affiliation and
ideological standpoints. In their seminal paper, Lazarsfeld et al. (1954)
distinguished between status homophily, when similarity in social status
fosters connections, and value homophily, when similarity in beliefs and ideas
tends to do so. In this view, the existing status homophily within these
groups would therefore be one of the main drivers behind value homophily and
thus contribute to affective polarization (McPherson et al., 2001).

To assess these competing hypotheses, this study focuses on the interactions
between different demographic groups in one of the main loci where opinions
are formed and challenged: the discussion of news (Kim et al., 1999). In
particular, the discussion of news on social media is of twofold interest.
First, it allows studying how specific demographic groups are connected in a
context where they cannot directly observe each other’s demographic
conditions: any measured effect is entirely due to differences in worldviews,
interests, and thoughts expressed through writing. Second, it allows us to
measure and compare, at the same time and in the same context, the likelihood
of interaction between opposing ideological sides. These reasons lead us to
investigate Reddit, one of the most visited websites in the U.S., a public
forum widely used to read and discuss news. We focus on the r/news subreddit,
the main Reddit community specifically dedicated to discussion of current
news, with a focus on U.S. internal affairs.

Therefore, our main research question is _whether social interactions in
online news discussions on Reddit tend to be segregated by demographic
boundaries, or by ideological echo chambers_.

To this aim, we first distinguish groups of users more likely to be part of a
certain demographic group thanks to the method proposed by Waller and Anderson
(2021). Then, we measure how much the amount of interactions observed between
demographic groups differs from a carefully-crafted null model, where such
attributes would not affect interactions. We replicate this analysis
separately for five years, from 2016 to 2020.

In all these experiments, we find similar effects. First, that ideological
echo chambers are absent: left-wing and right-wing users interact _more_ than
expected by a null model of random interactions, while within-group
interactions are less likely to happen. This result suggests that ideological
echo chambers do not necessarily appear in a typical context of opinion
formation online. Second, interactions in news discussions on Reddit are
unlikely to cross demographic boundaries, e.g., affluent users are more likely
to interact among themselves than with less-affluent users. In other words, we
observe _segregation_ along demographic boundaries, in the sense of
“restriction of contacts between various groups” (Freeman, 1978), in line with
previous literature on social networks (Hofstra et al., 2017). Finally, we
demonstrate that demographic homophily is not simply driven by common
interests, such as discussions on the same topics. While indeed different
demographic groups have different interests in the various news topics, their
segregation goes beyond this simple explanation. Most importantly, this
segregation happens without users being able to directly observe the
demographic attributes, and without the mediating effects of, e.g., geographic
segregation.

Our results, therefore, support the idea that not only echo chambers might not
be an intrinsic characteristic of social media (Cinelli et al., 2021), but
that the separation of demographic groups is observable during opinion
formation online: fundamental societal divisions might be reflected also in
social media.

##  2\. Background and related work

The polarization of opinions, with respect to politics and controversial
topics in general (Pew Research Center, 2017), has been reported to be on the
rise in recent years (Jacobson, 2016). Online social media, where billions of
people express their opinions daily, offer a scalable and inexpensive way to
quantify this phenomenon. The literature on this topic is too vast to review
here, so we briefly touch on a few landmark studies. In a pioneer study,
Conover et al. (2011) showed that on Twitter, a network of retweets about the
2010 US midterm elections exhibits a highly segregated partisan structure,
with extremely limited connectivity between left- and right-leaning users.
Since then, their result has been replicated several times, on different
topics, and in various contexts (Garimella et al., 2016; Cossard et al., 2020;
Garimella and Weber, 2017; Morales et al., 2015; Darwish, 2019). Opinion
polarization has also been detected on Facebook and YouTube, which may foster
selective exposure of users toward content they like, thus limiting the
exposure to diverse content (Bessi et al., 2016; An et al., 2014; Bakshy et
al., 2015; Harel et al., 2020; Quattrociocchi et al., 2016; Schmidt et al.,
2018). Echo chambers, situations where users have their beliefs reinforced due
to repeated interactions with like-minded peers, have been proposed as one of
the determinants for polarization on social media, and as a threat to
democracy (Sunstein, 2009).

The investigation around the presence of echo chambers on social media has
recently spurred a rich literature and a vivid debate among researchers. On
Twitter, the content consumption and production of users reveal a similar
political leaning, so that users are generally exposed to political opinions
that agree with their own (Garimella et al., 2018). On the same platform, the
presence of echo chambers in the debate about the impeachment of former
Brazilian President Dilma Rousseff has been shown to alter the information
diffusion between the two sides, supporters and opponents of the impeachment
(Cota et al., 2019). On Facebook, Quattrociocchi et al. (2016) showed that
users form polarized groups that share two distinct narratives, related to
conspiracy theories and science, respectively. On the same platform, the
content consumption about vaccines is skewed by echo chambers, while opinion
polarization increased over the years (Schmidt et al., 2018). Finally, Cinelli
et al. (2021) compared the presence of echo chambers around several
controversial topics across four social media platforms (Twitter, Facebook,
Reddit, and Gab), highlighting that Facebook shows higher segregation of news
consumption than Reddit. Interestingly, several works challenge the
harmfulness of the phenomenon (Sunstein, 2005), or the very existence of echo
chambers in different contexts (Dubois and Blank, 2018; Flaxman et al., 2016;
Guess et al., 2018). For instance, on Reddit, Trump and Clinton supporters
show a preference for cross-cutting political interactions between the two
communities rather than within-group interactions, thus contradicting the echo
chamber narrative (De Francisci Morales et al., 2021). The current work finds
a similar result in a different context (news discussion), and at the same
time finds evidence for demographic segregation on social media.

Since online discussion can be a driving factor of opinion formation (Kim et
al., 1999), the search for the causes of polarization has ultimately led to
questions being asked on how people interact on the base of their socio-
demographic extraction, culture, and ideology. Demographic segregation in
physical spaces has been the subject of study for several decades (Schelling,
1969), and has been shown to have increased in modern times (Logan and Parman,
2017), mostly due to racial sorting. The idea that this sorting is the main
cause for increased political polarization has been advanced (Bishop, 2009;
Levendusky, 2009), although it has also been criticized (Abrams and Fiorina,
2012). In this context, affective polarization has been introduced as a
measure of the attitude of individuals with opposite ideologies toward each
other (Iyengar et al., 2012). Iyengar and Westwood (2015) have shown how
partisan-wise polarization also leads to increasing animosity (and even
loathing) across party lines. Indeed, the social identities of people have
grown increasingly aligned with their partisan identity (Iyengar et al., 2019;
Mason and Wronski, 2018). Nonetheless, recent research has found how affective
polarization can be influenced by factors such as ethnicity (Bradley and
Chauchard, 2022) and age (Phillips, 2022), thus suggesting that socio-
demographic and cultural differences might be an important driving force in
social interactions. This idea is corroborated by observations of how social
interactions are strongly homophilic with respect to socio-demographic
extraction (McPherson et al., 2001; Lazarsfeld et al., 1954). All these
findings suggest that more attention is needed to the role of socio-
demographic and cultural features, as pivotal factors in influencing
individual social choices, and consequently opinion formation and
polarization.

To address this gap, we employ graph models that allow studying the interplay
between node features and link formation. Our choice of model is driven by a
few constraints. For instance, the well-known stochastic block model (Nowicki
and Snijders, 2001), in its typical form, only allows a node to belong to
exactly one class, while we are interested in modeling the presence of more
features on nodes at the same time. Also, we need a model able to capture
feature-feature relationships that are not limited to homophily. Thus, we opt
for the feature-feature model initially proposed by Miller et al. (2009) as a
way to learn features from data, and then further studied as a generative
network model (Boldi et al., 2016), and as a tool to infer feature-feature
relations (Monti and Boldi, 2017; Boldi and Monti, 2016). In the present work,
we use this network model, embedded inside a logistic regression, to evaluate
the statistical significance of the inferred correlations. Then, we extend it
to control for varying degrees of interest in news topics by different
demographic groups.

##  3\. Data

This section describes the data sources we use as input for our analyses.
First, Reddit and the r/news interaction graphs we built. Then, the socio-
demographic features extracted from Reddit participation data. Finally, the
news topics, that we employ as confounders.

###  3.1. Reddit interaction graph

Reddit is a social content aggregation website that has consistently ranked
among the top ten most visited websites in the United States over the past
years.222https://en.wikipedia.org/wiki/Reddit It is organized in topical
communities, called subreddits, centered around a variety of topics. In
particular, r/news, the focus of the current work, is dedicated to discussing
“news articles about current events in the United States and the rest of the
world”.333https://www.reddit.com/r/news Users can pseudonymously post
submissions in these subreddits, and comment on other submissions and
comments, thus creating a tree structure for the overall discussion.

Differently from other social media such as Facebook or Twitter, Reddit’s
homepage is organized around subreddits rather than user-to-user
relationships. As such, the subreddits chosen by a user represent the main
source of the information they consume on the website. We collect public data
from Reddit by using Pushift (Baumgartner et al., 2020).

Since Reddit data is massive—around $10^{7}$ comments each year—we reduce our
datasets to what we consider the most relevant interactions. First, we choose
users active on the subreddit r/news from 2016 to 2020, included. Then, we
select users by applying the following criteria: a minimum activity on r/news,
a minimum activity on the whole Reddit, and that they are not likely to be a
bot. In particular, a selected user must have written ($i$) at least 25
between submissions and comments on r/news in a given year,444We also perform
all the experiments with a threshold of 10 messages per year: this choice does
not significantly alter the results. and ($ii$) submissions to at least 5
different subreddits in a given year. These empirically-determined cutoffs
serve a twofold purpose: to select the most active users to avoid sparsity
issues and to reduce computational costs. Moreover, as we show next,
participation in other subreddits is necessary for estimating socio-
demographic features. To exclude news bots, we filter out users who either
($i$) made submissions on r/news but no comments, ($ii$) belong to a list of
known Reddit bots (e.g., automoderator), ($iii$) have posted in more than 50
subreddits each month, or ($iv$) have the string ’bot’ in their username.

Starting from the selected set of users $V$, we create an interaction graph
$G$ by considering the users as nodes and defining an arc $u\rightarrow v$ if
a user $u$ replies to a message posted by user $v$. Table 1 reports the number
of selected users and social interactions.

Table 1. Number of nodes and edges for the considered interaction graph of r/news in each year. Year | 2016 | 2017 | 2018 | 2019 | 2020
---|---|---|---|---|---
N. nodes | $27\,976$ | $34\,060$ | $31\,997$ | $21\,225$ | $29\,045$
N. edges | $1\,166\,076$ | $1\,390\,243$ | $1\,221\,779$ | $793\,569$ | $1\,067\,614$

###  3.2. Socio-demographic features

To extract the socio-demographic features of the users, we use existing
subreddit features obtained by embedding social interactions on Reddit. Waller
and Anderson (2021) assign a score for different demographic axes (such as
age, gender, and affluence) and social axes (political leaning) to each
subreddit. These scores are based on a set of well-thought pairs of seeds for
each axis (e.g., r/teenagers and r/redditforgrownups for age), which are then
used to construct the scores via embeddings, and finally validated with
external sources of information. We project these scores onto the users, and
define the score $F_{u}$ of user $u$ with respect to feature $F$ as the
weighted average of all their submissions in each subreddit,

(1) $F_{u}=\frac{\sum_{s}N_{u,s}F_{s}}{\sum_{s}{N_{u,s}}},$

where $s$ is a subreddit, $F_{s}$ is the feature score of subreddit $s$, and
$N_{u,s}$ is the number of submissions by user $u$ in subreddit $s$. For
instance, if a subreddit is leaning towards ‘male’ in the gender axis, (i.e.,
it is more likely to be frequented by males), this scoring method will assign
a larger probability of being male to a user who writes often on that
subreddit.

Equation 1 allows us to characterize each user by a set of continuous scores
that represent their position within each feature axis. To use these features
in a logistic regression model, we quantile-normalize these values among the
selected users, and—to streamline our analysis—we binarize them, labeling only
the ones with the highest and lowest quartile for each axis. For instance, we
label a user as male-leaning on the gender axis if their score falls in the
lowest quartile of the gender axis (similarly, as female-leaning if in the
highest quartile). We stress that a score indicating a female-leaning user
does not imply that such a user is necessarily female, as Reddit is known to
be participated by more men than women. Rather, it indicates a user that
frequents subreddits is more likely to be participated by women. Conversely, a
higher score on the age axis simply indicates that the user is more likely to
be older relative to the Reddit user base, which is quite young (Barthel et
al., 2016). Similarly, being in the lowest or highest quartile on the partisan
axis does not imply ideological extremeness, but rather, statistical
confidence about their partisan affiliation given their Reddit activity. Users
with very sparse or contradictory activity will instead be placed around the
median: as such, we will consider them as a baseline.

Figure 1. Distribution of topics across the selected submissions of r/news.

###  3.3. Topic classifier

To check whether the effects of socio-demographic attributes on interactions
are simply a byproduct of a varying degree of interest in news topics, we need
to classify posts in r/news according to their news topic. To do so, first, we
train a state-of-the-art supervised topic detection classifier on news article
headlines. Then, we apply this classifier to the news appearing in r/news, so
to obtain a mapping of posts into topics.

To train the supervised classifier we replicate results from the best-
performing news topic detection model from
Kaggle,555https://www.kaggle.com/code/hengzheng/news-category-classifier-val-
acc-0-65/ which we describe below. As a training set, we use a publicly
available data set gathered by Misra (2022). This data set collects news
headlines from the Huffington Post, each associated to the topic assigned by
the editorial staff according to their topic categorization. Since the posts
in r/news are links to newspaper articles, this data source is homogeneous
with the one we collect. The data set contains $210\,294$ news headlines
published between 2012 and 2022 and 40 topics. Then, we use GloVe embeddings
(Pennington et al., 2014) with a dimension of 100 to encode the words
identified after tokenization. Finally, we employ a long short-term memory
(LSTM) attention neural network to perform classification by using these
embeddings. In $10$-fold cross-validation on the news data set, this
classifier obtains an average accuracy of $65\%$, which is considerably high
considering that this is a 40-class classification problem.

After this training phase, we apply the resulting classifier to the
$3\,304\,044$ news headlines contained in our r/news data set and obtain a
mapping of posts into topics. Since each social interaction takes place under
a post, each link in our interaction network is associated to a single topic.

Figure 1 shows the 15 most popular topics in our data set. These 15 topics
account for $90\%$ of our data set; therefore, we exclude the remaining 27
topics from our analysis (the largest excluded topic accounts for $1.1\%$ of
the posts). The most popular topic is _Business_ ($18.2\%$ of posts), followed
by _Politics_ ($15.2\%$) and _Worldpost_ ($10.2\%$), a topic category related
to news events taking place outside the United States. The topic
_Entertainment_ ($8.4\%$) pertains mostly to news about movies and music.
_Healthy Living_ ($8.2\%$) collects news regarding healthy lifestyles and
habits. Other popular topics—beside self-explanatory ones such as _Crime_ ,
_Tech_ , and _Religion_ —include _Green_ , related to environment and climate
change; _Media_ , about TV and movies; _Style_ , about fashion and beauty
care; and _Weird News_ , collecting strange-but-true headlines, from UFOs to
pets. To further exemplify the topics, we report some headlines from our
Reddit dataset in Table 2.

Table 2. Sample of news headlines found in our Reddit data set for each considered topic. Topic |  News headline
---|---
Business |  Goldman’s Investment Bank to Increase its Hiring of Black Employees
Politics |  Trump attacks Schiff: ‘He lies awake at night shifting and turning’
Worldpost |  From China to Italy, Coronavirus leaves a trail of devastation
Entertainment |  Oscar 2020 Winners Full List
Healthy Living |  Nursing Homes, Racked by the Virus, Face a New Crisis: Isolation
Crime |  Video Shows Missouri Cop Run Over Fleeing Suspect With Car, Then Tackle Him As He Screams
Sports |  Moloney Vs Baez - Boxing, June 25, 2020, On ESPN
Travel |  Double rainbows form in skies over Delhi, surrounding areas
Green |  YouTube Has Been ‘Actively Promoting’ Videos Spreading Climate Denialism, According to New Report
Tech |  Parallels and Google team up to bring Windows apps to Chrome OS
Arts |  In Montana, the Art of Crafting Fly-Fishing Rods
Media |  Jim Lehrer, Longtime PBS NewsHour Anchor, Dies at 85
Style |  New year gifts
Weird News |  Cops: McDonald’s mask scofflaw breaks window, steals panties
Religion |  Join Wajahat Ali to discuss Ramadan during a pandemic

##  4\. Model

This section introduces the two logistic regression models we develop to
estimate the relationships among the socio-demographic groups in Reddit
r/news. Each one is built upon a network null model where the dependent
variable indicates whether a pair of nodes have interacted on the subreddit.
The first one, dubbed socio-demographic features model (SD), uses only
independent variables derived from the socio-demographic features. The second
model, the socio-demographic model with topics (SD+T), investigates whether
such relationships hold after controlling for different interest by each
demographic group in each news topic.

###  4.1. Socio-demographic features model

The goal of the SD model is to estimate which pairs of socio-demographic
features foster or inhibit interactions among Reddit users. To do so, we rely
on the following logistic regression approach, inspired by the feature-feature
graph model (Miller et al., 2009; Monti and Boldi, 2017; De Francisci Morales
et al., 2021). Consider a set of features $F$ and a directed graph $G=(V,E)$.
Each node $v\in V$ is characterized by a binary vector of features
$x_{v}\in\\{0,1\\}^{|F|}$. Then, for a node pair $(u,v)\in V\times V$, let the
dependent variable $y$ be equal to $1$ if the two nodes interact (i.e.,
$(u,v)\in E$) and $0$ otherwise. Our model estimates the relationship of the
dependent variable $y$ with the vectors of independent variables (features) of
the two nodes, $x_{u}$ and $x_{v}$. More specifically, we consider all the
possible pairs of features $F\times F$: the hypothesis is that the likelihood
of observing an interaction from $u$ to $v$ can be affected by node $u$ having
feature $h$ and node $v$ having feature $k$, for any feature pair $(h,k)\in
F\times F$. This model can be expressed by a feature-feature matrix
$W\in\mathbb{R}^{|F|\times|F|}$, where each element $W_{h,k}$ indicates the
log odds ratio for nodes with feature $h$ to interact with nodes with feature
$k$. A positive weight indicates that nodes with features $h$ are more likely
to interact with nodes with features $k$ with respect to the probability of
random interactions given by the null model (see below). Conversely, a
negative element $W_{h,k}$ indicates that this specific pair of features
inhibits interactions.

To convert this framework into a logistic regression model, we apply an outer-
product kernel (Monti and Boldi, 2017) to $x_{u},x_{v}$. In other words, the
independent variables of the logistic regression are all the elements of
$x_{u}\otimes x_{v}$; that is, each pair of nodes $u,v$ is associated to
$|F|\cdot|F|$ variables such that the element $h,k$ is $1$ if node $u$ has
feature $h$ and node $v$ has feature $k$. Therefore, the model can be
expressed as

(2)
$\operatorname{logit}(y_{u,v})=\beta_{0}+x_{u}^{\intercal}Wx_{v}=\beta_{0}+\sum_{h,k\in
F}x_{u,h}W_{h,k}x_{v,k}.$

where $\beta_{0}$ indicates the intercept, which adjusts the model according
to the average probability of an interaction between two nodes.

In our context, the directed graph $G=(V,E)$ is the graph of interactions in
Reddit r/news in a given year, as defined in Section 3.1. The features used in
this model are the socio-demographic variables defined in Section 3.2. The set
$F$ is therefore composed by features: Young, Old, Male, Female, Poor, Rich,
Left-leaning, and Right-leaning. We assign such features to nodes as explained
in Section 3.2, so that $x_{u,k}$ is $1$ iff node $u$ is in the quartile of
considered users more likely to be in socio-demographic group $k$. This way,
the logistic regression model fits a given sequence of pairs
$(y_{u,v},x_{u}\otimes x_{v})$, and obtains an estimate of the feature-feature
matrix $W$. Such a matrix indicates which socio-demographic groups are more
likely to interact.

The construction of the sequence of node pairs is crucial, as it represents
the null model. We would like this null model to be the random graph obtained
by reshuffling the links of the original network, while preserving the in- and
out-degree of each node—that is, a configuration model (Molloy and Reed,
1995): this way, the activity of each user is properly taken into account.
Therefore, we define a balanced sequence of pairs, i.e., we sample a number of
negative examples equal to the positive ones. We do so by, first, including
all positive examples (i.e., the links $E$); then, as negative examples, we
choose node $u$ with probability proportional to their activity (i.e., the
number of posted comments), and node $v$ with probability proportional to
their attractiveness (the number of received comments) (De Francisci Morales
et al., 2021). If the obtained pair $(u,v)$ is a link, we discard it. This
way, the null model expresses the probability of considering a pair of nodes
as the product of two independent probabilities—the probability that a node
initiates an interaction, and the probability that a node receives it. The
estimated matrix $W$ will therefore represent how the socio-demographic
features make the observed links deviate from this null model. Since the
estimation of matrix $W$ is obtained from a logistic regression model, it is
also accompanied by a statistical test able to tell if each element $W_{h,k}$
is significantly different from zero or if it might be spurious.

###  4.2. Socio-demographic model with topics

One might wonder whether the status homophily we see is just the result of
shared interests. That is, similar people are interested in and comment on
similar topics, and therefore are more likely to interact simply because they
are drawn towards the same posts. In other words, we wish to assess whether
the topic of the post (news) acts as a mediator (Pearl, 2009), or conversely,
whether there are any residual effects of the socio-demographic features once
we control for the topic, which may act as a confounder (Pearl, 2009).

To this aim, we augment the SD model to account for the topic of the post by
introducing a feature-topic matrix $Q\in\mathbb{R}^{|F|\times|T|}$, where $T$
is the set of possible topics defined in Section 3.3. Each entry $Q_{h,t}$ of
the matrix represents the log odds ratio that a user with feature $h$ is
interested in topic $t$ (and therefore comments on it). To formalize the model
we make use of a binary indicator (one-hot) vector
$\tau^{p}\in\\{0,1\\}^{|T|}$ which for each post $p$ (on which the interaction
between $u$ and $v$ happens) has value $1$ only for the topic of the post, and
is $0$ otherwise.

We integrate this feature-topic component $Q$ into the model expressed by
Equation 2 by assuming that, beside the feature-feature effects expressed by
$W$, the probability of observing a link $u\rightarrow v$ on topic $t$ is also
affected by the interest of user $u$ in topic $t$, and the interest of user
$v$ in the same topic. Thus, we can formally express this model is as follows:

(3)
$\begin{split}\operatorname{logit}(y_{u,v})=\;&\beta_{0}+x_{u}^{\intercal}Wx_{v}+x_{u}^{\intercal}Q\tau^{p}+x_{v}^{\intercal}Q\tau^{p}=\\\
&\beta_{0}+\sum_{h,k\in F}x_{u,h}W_{h,k}x_{v,k}+\sum_{h\in
F}x_{u,h}Q_{h,t}+\sum_{k\in F}x_{v,k}Q_{k,t},\end{split}$

where $t$ represents the index of the topic of post $p$ in $T$.

The matrix $Q$ is the same for both the initiator and the receiver of the
interaction, i.e., it is shared between $x_{u}$ and $x_{v}$. In fact, it is
also possible to write the topic terms of Equation (3) as
$(x_{u}+x_{v})^{\intercal}Q\tau^{p}$, where $(x_{u}+x_{v})_{k}$ indicates
therefore the number of participants in the interaction with feature $k$. As
such, $Q$ represents a non-directional effect that captures the interest of
socio-demographic groups in each news topic. In addition, the increase in
complexity of the model is limited: we need to estimate only an additional
$|F|\cdot|T|$ parameters. If the status homophily we measure is due simply to
similar interests, we would expect the parameters $Q$ to capture most of the
effect, and therefore make $W$ not significant.

##  5\. Results

We now use the data we gathered in combination with these models to answer our
research question. We articulate our main research goal into the following
research questions:

  1. RQ1

Are there ideological echo chambers in r/news? I.e., are interactions among
users on the same political side more likely?

  2. RQ2

Is there evidence of demographic segregation? I.e., are interactions among
users within the same demographic group more likely?

  3. RQ3

Do our findings on RQ1 and RQ2 hold after controlling for varying degrees of
interests in news topics by different demographic groups?

  4. RQ4

Which news topic is each demographic group more interested in?

First, we use the SD model to assess whether interactions are more homophilic
or heterophilic with respect to ideological (RQ1) and demographic attributes
(RQ2). Then, we use the SD+T model to investigate whether such results hold
after controlling for the varying degree of interest in news topics (RQ3).
Finally, we analyze the results of the latter model to study which news topics
are more attractive for the different socio-demographic groups (RQ4).

###  5.1. SD model

Figure 2. Logistic regression coefficients for interactions between all pairs
of demographic features obtained for the r/news subreddit in 2016 by the
socio-demographic features model. We show only coefficients that are
significant in at least 4 years out of 5.

We apply the SD model to the interaction graph, using the $8$ socio-
demographic features we defined, separately for each considered year (2016 to
2020). We obtain an estimate of the feature-feature matrix $W$ for each year.
As a first representation of our results, Figure 2 shows the matrix of one
year (2016), but shows only the coefficients that are statistically
significant in at least four years out of five. More specifically, in this
context, we define a coefficient $W_{h,k}$ as significant if the null
hypothesis of $W_{h,k}=0$ is rejected ($p$-value $<0.05$), and the coefficient
$W_{h,k}$ has the same sign across all the considered years. The base case for
the model is a zero-valued feature vector, i.e., a user who is not in the top
or bottom quartile on any socio-demographic axis.

Observing Figure 2, the most striking characteristic is the diagonal—i.e.,
each socio-demographic group tends to interact significantly more, or less,
with members of the same group. In other words, each pair of features display
a consistent effect in terms of heterophily and homophily. Remarkably, this
effect is heterophily for the political leaning and homophily for the
demographic features. Left-leaning and right-leaning users interact
significantly more across the boundary, and significantly less within their
respective group, w.r.t. the null model. Interactions with the opposite
ideological camp are the norm and not the exception in r/news: our answer to
RQ1 is therefore negative. Conversely, regarding age, gender, and affluence,
users tend to interact significantly more _within_ their demographic group.
For age and affluence, in particular, we observe a consistent tendency of
users to segregate: belonging to different groups inhibits interactions
between users, thus leading to a positive answer to RQ2.

Figure 3. Logistic regression coefficients for interactions between socio-
demographic features obtained (independently) for each year in the r/news
subreddit in the SD model.

Since we observe that most of the significant coefficients belong to
interactions between pairs of demographic features, we further investigate
them in Figure 3. In particular, we show the block-diagonal of the weight
matrix for each socio-demographic axis, one per subplot. On the x-axis we
report the year, on the y-axis the value of the $W$ coefficient (error bars
represent the $95\%$ CI), solid lines represent within-class interactions, and
dotted lines represent cross-class ones. Again, the most important global
pattern that emerges from the figure is that, for demographic features,
within-class interactions are more frequent than expected, while the opposite
is true for political leaning. Overall, interactions on r/news are
characterized by _status homophily_ and _value heterophily_.

The pattern is consistent and statistically significant over the years, with
the exception of the gender axis, where the within-class interactions are not
far from the null model. We also see a general trend towards convergence as we
get closer to 2020 (especially pronounced in the age and leaning axes). This
effect might be due to the highly polarizing 2016 US presidential election,
which saw an important participation on Reddit, while in 2020 Trump supporters
were banned from Reddit, and resorted to alternative platforms such as Gab and
Parler (Zhou et al., 2018; Aliapoulios et al., 2021).

###  5.2. SD+T model

We now move to the socio-demographic model with topics, which controls for the
topic of the discussion where the interaction takes place. Figure 4 shows the
weights of the matrix W for the logistic regression model of socio-demographic
model with topics (again, the block-diagonal for each axis). The main result
is that the patterns do not change much from what we observed in Figure 3,
which leads to a positive answer to RQ3. Indeed, we observe again status
homophily and value heterophily, as within-class interactions along the age,
gender, and affluence axes are more likely than expected, while cross-class
ones are more likely for political leaning. The same trends are present also
in this case: convergence towards zero as we approach 2020, and non-
significance of the cross-class gender terms. Overall, controlling for the
topic does not alter the results of the model, although it considerably
improves its fit. This fact indicates that the topic acts as a confounder and
not as a mediator: while the homophily and heterophily effects are not caused
solely by the topic of the post, interactions are nonetheless partially driven
by the different interests of demographic groups in different topics.

Figure 4. Logistic regression coefficients for interactions between
demographic features obtained (independently) for each year in the r/news
subreddit in the SD+T model. Figure 5. Logistic regression coefficients (and
$95\%$ CI) for interactions between demographic features and topics obtained
for the r/news subreddit in 2016 by the SD+T model. Filled disks indicate
associations significant in at least 4 years out of 5.

Therefore, we look at the coefficients of the topic-class interactions encoded
in matrix $Q$ of socio-demographic model with topics in order to answer RQ4.
Figure 5 shows the weights of the matrix for one year (2016, the other years
are qualitatively similar). Each row represents a topic, each column an axis,
and the value for each coefficient is full if it is statistically significant
at least $4$ out of $5$ years, otherwise, it is hollow. The error bars
represent the $95\%$ CI of the estimates. A few interesting observations can
be made.

On the age axis, younger people are more interested in arts, technology, and
entertainment, while older users are more interested in business and politics.
Older users are also more interested in green/environment topics, which might
seem counter-intuitive, but can be understood by looking at the demographic of
Reddit’s user base, where older users are in their 30’s or 40’s, and younger
ones are in their teens. On the gender axis, typically male users show more
interest in technology and business, but also lifestyle. More female users are
interested in religion, weird news, healthy living, and lifestyle. On the
affluence axis, low-income users are interested in religion and world news,
while in general, they seem less interested in environment, travel, healthy
living, business, and sports. Conversely, richer users are interested in
technology, travel, business, and to a lesser degree in healthy living and
entertainment.

Finally, on the political leaning axis, we see a strong effect for right-wing
users who are clearly less interested in arts and lifestyle news, and also
show disinterest in several other topics including technology, environment,
healthy living, entertainment, and business. The only sizeable positive
coefficients are for world news and crime. Left-leaning users, instead, show
an interest in arts, media, and entertainment.

##  6\. Discussion

We have studied social interactions among Reddit users in an opinion formation
context: the discussion of news on the subreddit r/news. By looking at the
process through a socio-demographic lens, we have uncovered informative and
consistent patterns. Specifically, the interaction network displays clear
status homophily, whereby users similar in age, gender, and affluence, are
more likely to interact. In addition, we find value heterophily, as users with
opposing political leaning are also more likely to interact. Importantly,
these patterns are robust to controlling for the interest in the topic of the
news: the preference of a socio-demographic group for a news topic is not
sufficient to explain the bias we observe. Even inside the same news topic,
users’ interactions are biased toward demographic homophily and ideological
heterophily.

Overall, these results paint a picture where ideological echo chambers are not
evident in news discussions, while we find evidence of demographic
segregation. Strikingly, this demographic segregation happens even though the
demographic traits at play are not immediately available to the discussants.
Indeed, as the discussions happens online and in writing, the age, gender, and
affluence of the user is a piece of latent information. Nevertheless, these
traits affect the people with whom one chooses to interact.

The most likely explanation for this result supports the theory that one’s
worldview is affected by the demographic group they belong to. These
worldviews, in turn, are evident in the opinions expressed through writing on
online social media, which in turn drives the homophilic interaction process.
This result runs against the common narrative of the Web and the Internet as a
‘global village’ (McLuhan, 1994). Note that Harteveld (2021) showed that
higher levels of demographic segregation are associated with higher levels of
affective polarization. As such, our results support the hypothesis that the
increase in affective polarization observed over the last decades might be
more associated with an increasingly divided society than with online echo
chambers.

Our findings also confirm that there is no ideological selective exposure in
news discussions on Reddit. While it is known that more niche subreddits might
form echo chambers, with users moving within ideologically uniform communities
(Rollo et al., 2022), our results strengthen previous analyses about the lack
of ideological echo chambers in generalist Reddit communities (De Francisci
Morales et al., 2021). Conversely, echo chambers have been reportedly observed
on social media such as Twitter and Facebook (Conover et al., 2011; Garimella
et al., 2018; Bakshy et al., 2015; An et al., 2014). Cinelli et al. (2021)
have advanced the hypothesis that this difference between Reddit and other
social media derives from their different structure: interest-based forum
versus interpersonal social networks. Future work should investigate if, on
these social networks, demographic segregation also contributes to affective
polarization.

Our analysis also revealed some interesting patterns about the preferences of
different socio-demographic groups toward certain news topics. It is already
known that newspapers and media producers target news content to their
audience, given their advertising-based financial model (Elejalde et al.,
2019). Some of these preferences have also been reported in the literature.
For instance, Meeter et al. (2010) find that gender influences news retention
about business news, both age and educational attainment affect political
news, and educational attainment also had an effect on science news. It is
reasonable to expect that also news consumption would be affected, and indeed,
the business topic shows a marked distinction on the gender axis (with males
more interested in the topic), and age has an effect on the interest in
political news (older users are more interested).

In a similar spirit, Lee and Chyi (2014) investigate the relationship between
demographic variables and the noteworthiness of news. They find that local
news is perceived as more noteworthy by women, older, and less affluent
people. In contrast, political and international news is favored by older and
more educated people. Business and sports are quite different, with the former
perceived as more noteworthy by older, more educated, and more affluent
people, while the latter is more noteworthy for men and white people. Finally,
entertainment is one category where younger adults have more interest than
older people. On all the demographic variables that we also measure, our
results are in line with what was found by Lee and Chyi (2014), who used a
completely different methodology. It is well known that different news sources
have different attractiveness for different socio-demographic groups
(Rosenberg, nter). For instance, male and wealthy audiences prefer business
news, such as the ones published on the Economist and the Wall St. Journal
(Center, [n. d.]). We see this effect in our results.

Conversely, entertainment news is often associated with a young and female
audience (Hamilton, 2011). We see more mixed evidence for this pattern, with,
for instance, entertainment news being more engaging for younger users, but
without any effect for gender, and sports news with a similar effect for age
but being more engaging for a male audience. Healthy living and weird news,
arguably two of the main soft news categories, show the opposite pattern, with
a more female audience but no difference in age.

These effects suggest that, through demographic segmentation of the audience,
online news might be contributing to widening the rift between demographic
groups. For instance, demographic segmentation has been observed even in
political advertising on social media, where different demographic groups
witness different messages (Capozzi et al., 2021). However, this work shows
that the separation between demographic groups goes beyond their interests in
different news topics. Social media users are self-sorting (Harteveld, 2021)
by demographic groups, whereby interactions across groups are less likely. The
widening rift between demographic groups, whose worldviews and material
conditions are growing apart, is a phenomenon that should be taken more into
consideration, even in social media analysis.

This finding suggests a few research directions to overcome the limitations of
the present work. On a methodological level, this study is limited to a few
demographic traits, estimated via a black-box approach. Firstly, it would be
important to develop a white-box, probabilistic assessment of demographic
groups online: having a Bayesian estimate would help to quantify the
statistical significance of segregation patterns. Secondly, the inclusion of
more demographic attributes beyond gender, age, and affluence, could highlight
other important phenomena in online social interactions. Furthermore, we
restrict our analysis to r/news: other venues for online discussions might
present different characteristics. Beyond improving the methodology and
broadening the scope, in future works one should investigate the causal
pathways that connect demographic segregation and affective polarization. In
this work, we have quantitatively compared two competing hypotheses to explain
affective polarization and found that demographic segregation is more
prominent than ideological echo chambers on Reddit news discussions. More
analysis of the causal connections between these important aspects of our
society is urgently needed.

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