# Tracking a Year of Polarized Twitter Discourse on Abortion

Ashwin Rao
Department of Computer Science
University of Southern California
Los Angeles, CA
<EMAIL_ADDRESS>
Rong-Ching Chang
Department of Computer Science
University of California-Davis
Davis, CA
<EMAIL_ADDRESS>
Qiankun Zhong
Department of Communication
University of California-Davis
Davis, CA
<EMAIL_ADDRESS>
Kristina Lerman
Information Sciences Institute
University of Southern California
Marina del Rey, CA
<EMAIL_ADDRESS>
Magdalena Wojcieszak
Department of Communication
University of California-Davis
Davis, CA
<EMAIL_ADDRESS>

###### Abstract

Abortion is one of the most contentious issues in American politics. The Dobbs
v. Jackson Women’s Health Organization ruling in 2022, which shifted the
authority to regulate abortion from the federal government to the states,
triggering intense protests and emotional debates across the nation. Yet,
little is known about how online discourse about abortion rights fluctuated on
social media platforms. This study analyzes a corpus of over 57M abortion-
related tweets from January 2022 to January 2023 to show how emotions, hateful
rhetoric, toxic speech, use of obscenities and insults, and also framing
strategies fluctuated over the span of one year among liberal and conservative
users. We offer three key findings. (1) Fluctuations in emotions were
temporary; key events during the analyzed period did not bring about lasting
shifts in expressed emotions. (2) We observe significant ideological
differences in the use of hate speech: conservatives resorted to hateful
rhetoric more than liberals. Yet, liberals were especially likely to use
obscenities and insults, especially on the days the ruling was leaked and
after the Dobbs decision. In turn, toxic language sharply increased among both
groups following the leak and after the SCOTUS ruling. (3) Conservatives
employ religious and fetal personhood frames, while liberals emphasize women’s
health and bodily autonomy, with each group reacting negatively to the other
group’s frames. Our results offer an in-depth insight into the dynamics of
online discourse on one of the most contentious issues in contemporary
America.

_Keywords_ Abortion $\cdot$ Twitter $\cdot$ Emotions $\cdot$ Hate speech
$\cdot$ Toxicity

##  1 Introduction

Abortion remains one of the most contentious, divisive, and emotionally
charged issues in American society. Survey data suggest that Democrats and
Republicans have grown increasingly polarized on abortion rights Saad (2010a,
b); DiMaggio et al. (1996); Evans (2003), disagreeing on its morality and the
circumstances in which it should be legal Brenan (2010). In addition, violence
against abortion providers and patients rose by a staggering $128\%$ in the
past few years NAF (2022). Polarization further increased in the aftermath of
the historic ruling in Dobbs v. Jackson Women’s Health Organization on June
24, 2022. The United States Supreme Court struck down federal protections made
available in Roe v. Wade (1973) and Casey v. Planned Parenthood (1992) and
transferred power to regulate it to the states. As a result, women across the
U.S. lost the constitutional right to abortion. When delivering the opinion of
the court, Justice Samuel Alito noted that abortion presents a profound moral
issue on which Americans hold sharply contrasting views SCOTUS (2022).

The leaked draft of the Supreme Court’s decision on May 2, 2022, ignited
passions on both sides. Activists took to the streets and online platforms
with slogans like “Bans Off Our Bodies” and “Equal Rights for the Unborn,”
reflecting the emotionally charged mood of a deeply divided populace. Despite
the availability of online behavioral data and the fact that it can offer
unprecedented insights into the fluctuations in public opinion, we know
relatively little about how online discussions and collective emotions in the
abortion rights debate evolved, especially after the overturning of Roe v.
Wade. Several pivotal questions remain unexplored: How did public emotions
fluctuate in response to external events? How did the tone of discussions
change: did they remain civil or did they devolve into toxicity and hate? Were
there discernible variations in these online expressions based on ideological
stances? How did conservatives and liberals frame the discourse on abortion
access, especially across blue and red states? How did each group react to the
frames used by the other? These questions are central to our understanding of
the nuanced expressions surrounding abortion and the specific context of Roe
v. Wade’s reversal.

To fill these gaps in knowledge, we collect and analyze a corpus of over 57M
tweets related to abortion over the span of one year (January 2022 to January
2023) from over 5.5M users. The large volume of messages about abortion
coupled with advances in natural language processing and quantitative text
analysis enable us to characterize public opinion expressions at scale. We
estimate users’ ideology based on their URL sharing behaviors Rao et al.
(2021); Cinelli et al. (2021) (SI Appendix 44.1) and rely on state-of-the-art
transformer-based classifiers to identify emotions, hateful and toxic speech,
insults and obscenities expressed in the tweets (see SI Appendix
44.3.2,44.3.3,44.3.4). We show the fluctuations in these expressions and also
use interrupted time series to quantify the immediate effects of key events on
emotions, hateful and toxic speech, insults, and obscenities by users’
ideology. Lastly, relying on linguistic analysis techniques (see Methods and
SI Section 4.5), we extract five prominent frames utilized by liberals and
conservatives in their discussions of abortion, including religious
justifications, opinions on bodily autonomy, considerations for women’s
health, allowance for exceptions, and the assertion of fetal personhood. Using
geolocation inference, we highlight geographic differences in activity with
respect to these five frames and contrast the emotions of liberals and
conservatives in these discussions.

We use these extensive and granular social media data to address the following
research questions: (1) How did emotions, hateful rhetoric, toxic speech,
insults, and obscenities in tweets about abortion vary over time among
liberals and conservatives? (2) Were there ideological asymmetries in the use
of emotions, hate speech, toxic language, obscenities, and insults in response
to key events within the abortion rights debate (i.e., the leak, the official
Dobbs ruling, the Kansas referendum, and the U.S. midterm elections)? (3) What
were the prominent frames employed by liberals and conservatives when
discussing abortion rights, and were there variations in emotions and by
geographical location in these frames?

Public behavioral data collected from social media platforms, coupled with
advanced linguistic analysis tools, provide researchers a novel avenue to
study public opinion and individual expressions at granular level and over-
time. Such data offer several advantages over survey-based measurements,
allowing researchers to assess individual political leanings and issue stances
without biases inherent in survey self-reports, especially on contentious
political issues (e.g., social desirability bias). Large-scale social media
datasets are used to analyze protest mobilization Breuer et al. (2015);
Steinert-Threlkeld (2017); Munn (2021), misinformation spread Nikolov et al.
(2020); Rao et al. (2021); Chen et al. (2021), moral attitudes Guo et al.
(2022); Priniski et al. (2021), echo chambers and ideological biases on
platforms Barberá (2015); Wojcieszak et al. (2022), among other phenomena.
Social media data are also used to examine how online discourse reflects and
shapes societal debates about controversial topics Yardi and Boyd (2010);
Garimella et al. (2018); Cinelli et al. (2021), including abortion, finding
that those debates on social media are deeply polarized with distinctive
textual and psycho-linguistic cues Sharma et al. (2017). The analysis of
large-scale social media data at pivotal points like the COVID-19 pandemic
Huang et al. (2020); Chen et al. (2020), 2020 US Presidential elections Chen
et al. (2022); Abilov et al. (2021), or the Russian invasion of Ukraine Chen
and Ferrara (2022) helps researchers capture the pulse of an increasingly
volatile world. In this project, we contribute to this research, capturing
another pivotal point in the American history, the overturn of Roe vs. Wade.

We offer several key findings. First, fluctuations in anger, anxiety, sadness,
and joy were temporary; key events during the analyzed twelve months (e.g.,
the leak, the Supreme Court decision, the midterm elections) did not bring
about lasting emotional shifts in online discourse among liberals and
conservatives. Instead, emotions oscillated, reflecting the collective mood of
each group, and reverted to the baseline shortly after. We observe significant
ideological differences in the use of hate speech: conservatives resorted to
hateful rhetoric more than liberals. Yet, liberals were especially likely to
use obscenities and insults, especially on the days the ruling was leaked and
after the Dobbs decision. In turn, toxic language sharply increased among both
groups following the leak and especially after the SCOTUS ruling. Third, we
shed light on how liberals and conservatives frame their online discussions
about abortion. Conservatives employ religious and fetal personhood frames,
while liberals emphasize women’s health and bodily autonomy. Furthermore, the
frames favored by one side provoke strong negative reactions from the other:
liberals express more anger and toxicity when discussing religion, while
conservatives use more toxic language and hate speech when addressing women’s
health. The increased expression of anger, fear, and toxic rhetoric among
liberals regarding frames central to conservative abortion discourse,
paralleled by a corresponding increase in negative emotions among
conservatives when addressing issues pivotal to liberal discourse underscores
the stark divisions between these two ideological groups. Although these
patterns may be expected, ours is the first study to show key fluctuations in
abortion discourse using online behavioral data from millions of partisan
Twitter users in the United States.

Our study sheds light on how the polarized abortion debate post Dobbs is
shaping the US political landscape. We find that key events elicit distinct
emotional responses from ideological groups, and frames that resonate with one
side often trigger strong negative reactions from the other, highlighting the
polarization inherent in this contentious issue. Through the lens of social
media data, we capture a pivotal moment in American history–—the overturning
of Roe v. Wade–—offering valuable insights into the evolving landscape of
public opinion and political discourse in the digital age. Our research
contributes to the broader understanding of how online discussions reflect and
influence societal debates on complex and divisive topics.

##  2 Data and Methods

### Data

In this study, we leverage the large-scale Twitter dataset we collected Chang
et al. (2023), comprising of tweets about abortion rights between January 1,
2022 to January 6, 2023. Relying on a carefully curated list of keywords and
hashtags that reflect both sides of the abortion rights debate in the United
States, Chang et al. (2023) identifies over $57,540,676$ tweets generated by
$5,426,555$ users. Given that this dataset predates both Politico’s leak of
SCOTUS’s draft judgment on May 3, 2022 and SCOTUS’s June 24,2022 official
ruling in Dobbs vs Jackson Women’s Health Organization, makes possible the
analysis of assessing the impact of both these events.

In order to identify tweets generated from within the United States and also
to identify the location of the users (whether in red or blue state), we
utilize Carmen Dredze et al. (2013), a geo-location inference technique that
relies solely on tweet metadata. This includes the “place” and “coordinates”
objects that encode location information, such as country, city, and
geographic coordinates. Additionally, the technique also relies on mentions of
locations in a user’s bio on Twitter to infer their location. A manual review
confirmed that this approach was effective in identifying a user’s home state.
As a result, we were left with roughly $12,912,344$ tweets generated by
$1,084,991$ users in the United States. Additional statistics about the per-
state activity are available under the SI Appendix 4.2.

We leverage state-of-the-art statistical inference techniques and transformer
based language models to infer at the user level - ideology (liberal or
conservative) and – at the tweet level – basic emotions Plutchik (1980)
expressed - anger, fear, sadness, and joy, hate and toxic speech, obscenities
and insults.

### User Ideology

Extensive work estimates ideology using social media data. Previous studies
have relied on hashtags Conover et al. (2011), following relationships,
network interactions Barberá (2015); Badawy et al. (2018), and URL sharing
behaviors Le et al. (2019); Nikolov et al. (2020); Cinelli et al. (2021); Rao
et al. (2021). In this study, we rely on the method discussed in Rao et al.
(2021) to quantify ideology at the user level. This method works by first
computing the weighted average of ideological leanings of the URLs users
share. The scores are then used to train a linear classifier with embeddings
of users’ tweets obtained from a pretrained fastText embedding model
Pagliardini et al. (2018) as features. The trained linear classifier is then
used to estimate ideological leaning, liberal or conservative for all users in
our dataset. Refer to the SI Appendix 44.1 for a detailed description of the
approach.

### Inferring Geolocation

In order to identify locations of tweets, we used Carmen Dredze et al. (2013),
a geolocation inference tool. Carmen harnesses a multitude of signals within
the tweet object to carry out this inference process. While geolocation
information, both provided by the user and inferred by Twitter, is directly
accessible within the tweet-object under the “coordinates” and “place”
objects, it’s worth noting that this data is available in less than $5\%$ of
the tweets in our dataset. However, a significant portion of users often
include location details, at least down to the city or state of residence,
within their profile biographies. Carmen utilizes this additional information
to infer geolocation data at the county, state, and country levels. State
level geolocation statistics are shown in SI Appendix 4.2 in Table 6.

### Emotions

Most motion detection approaches are based on one of two theories - Ekman’s
Theory of Emotions Ekman and Davidson (1994) and, Plutchik’s Wheel of Emotions
Plutchik (1980). Ekman’s theory finds that facial expressions are universal
indicators of basic emotions and identifies six primary emotions as
universally recognized across cultures - happiness, sadness, anger, fear,
disgust, and surprise. Plutchik’s theory, in turn, categorizes emotions into
eight primary emotions, positioned on a colored wheel and that other emotions
are a combination of these primary emotions. These include joy, trust, fear,
surprise, sadness, disgust, anger, and anticipation.

Traditionally emotions and sentiment have been quantified using lexicon based
approaches Tausczik and Pennebaker (2010); Mohammad and Turney (2013); Hutto
and Gilbert (2014). While sentiment classification Tausczik and Pennebaker
(2010); Hutto and Gilbert (2014) categorizes text into positive, negative and
neutral, emotion classification Mohammad and Turney (2013) delves intointo
nuanced emotions. Lexicon based approaches however suffer from a lack of
ability to judge context. The advent of transformers Vaswani et al. (2017) and
large-language models Devlin et al. (2018); Liu et al. (2019) enabled
researchers to design approaches that considered context.

In this study, for quantifying emotions expressed in tweets, we rely on
SpanEmo, a BERT-based state-of-the-art emotions classifier Alhuzali and
Ananiadou (2021), fine-tuned on the SemEval 2018 Task 1e-c Mohammad et al.
(2018) dataset. This classifier identifies 11 different emotions - anger,
disgust, fear, joy, sadness, love, anticipation, optimism, pessimism, surprise
and trust, described under the Plutchik’s wheel of emotions Plutchik (1980). A
recent study Jack et al. (2014) however, suggests that there are four basic
emotions - anger, joy, sadness and fear. We limit the focus of this work to
these four emotions. Validation of this emotion classifier against these four
emotions was performed against human annotated data and is further discussed
in SI Appendix 4.3 under Table 10. The codebook used in gathering human
annotations for a subset of the data is discussed in SI Appendix 4.3 under
Table 4.3.6.

### Hate speech

Hate speech, commonly defined as any form of communication expressing
discrimination, hostility, or prejudice against individuals or groups based on
attributes such as race, ethnicity, nationality, religion, gender, sexual
orientation, disability, and other characteristics, has been a subject of
extensive research in the realm of social media content identification Waseem
and Hovy (2016); Davidson et al. (2017); Gambäck and Sikdar (2017); Barbieri
et al. (2020); Siegel et al. (2021); Calabrese et al. (2022). Similar to
challenges faced in emotion detection, lexicon-based approaches Siegel et al.
(2021) often encounter false positives due to the absence of contextual cues.

In this work, we employ a state-of-the-art hate speech detection classifier,
specifically a RoBERTa-based model Liu et al. (2019), fine-tuned using the
SemEval 2019 Task 5 dataset Basile et al. (2019). This dataset was designed to
identify hate speech directed at women and immigrants, and is well suited for
the objectives of this study. The binary classifier categorizes tweets as
either hate speech or not. Validation of the hate speech classifier was
performed against human annotated data. The codebook used in gathering human
annotations for a subset of the data is discussed in SI Appendix 4.3 under
Table 4.3.6.

### Toxicity

Toxic speech is a broader term encompassing varied forms of harmful or abusive
communication that can include hate speech but is not limited to it. Toxic
speech, unlike hate speech, is not targeted towards specific groups or
identities. Unitary AI’s Detoxify Hanu and Unitary team (2020) and Google
Jigsaw’s Perspective API Google-Jigsaw (2021) are two current state-of-the-art
multilabel deep learning models that identify toxic and abusive speech in
text. While both APIs aim to primarily identify toxicity they also identify
severe toxicity, profanity, insults, threats, identity attacks and sexually
explicit content. In this study, we rely on Detoxify Hanu and Unitary team
(2020) to identify toxic speech and also insults and offensive language.

### Interrupted Time Series

Interrupted time series analysis Wagner et al. (2002) allows us to disentangle
the heterogeneous effects of user ideology on affect. Previous works Bernal et
al. (2017); Green et al. (2021); Wang and Inbar (2021) have leveraged this
approach to quantify the discontinuity introduced in trends post exposure to a
certain treatment. We define two separate regression models, one for each
ideological group, to identify discontinuities in trends in response to a
sociopolitical event. The models are specified as:

$Y_{w}=\beta_{0}+\beta_{1}time_{w}+\beta_{2}treatment_{w}\\\
+\beta_{3}time\,post\,treatment_{w}+\epsilon$

where, $Y_{w}$ the outcome variable, is the share of tweets that in a 4-hour
window $w$ that express a particular affect. $time$ is a continuous variable
indicating the time passed in number of 4-hour windows since the occurrence of
the event (treatment), treatment is a dummy variable encoding whether the time
window $w$ was before or after the event (before = 0, after = 1) and, days
post treatment is a continuous variable with 0 before the treatment and the
number of 4-hour windows passed since the treatment. $\beta_{1}$ is the slope
of the trend in affect before the event, $\beta_{3}$ is the change in slope
post-event, and $\beta_{2}$ is the “jump” in affect immediately post the
event’s occurrence. We model this separately for liberals and conservatives
for each expression of interest (i.e., emotions, hate speech, toxicity,
insults, and obscenities) and using $\beta_{2}$, we test the hypothesis that a
given expression increased/decreased for a particular group immediately post-
event.

#### Frame Identification

To identify and measure the prevalence of key aspects of the abortion rights
debate in the U.S., we first curated phrases and keywords that can identify
discourse related to religion, bodily autonomy, fetal personhood, women’s
rights and fetal viability in the context of the abortion, five frames
identified as central in past work on abortion-related considerations and
arguments Marquis (1989); Harris and Mills (1985); Cohen (2015); Greasley
(2017); Mackenzie (2020); Pew (2022); Hartig (2022). The curation of these
phrases and keywords was done by leveraging, SAGE, a keyword extraction method
discussed in Eisenstein et al. (2011). SAGE identifies keywords by assigning
each word in a document a score that represents its prominence compared to a
baseline document. More specifically, SAGE calculates the deviation in log-
frequencies of words from a baseline lexical distribution.

We applied SAGE on frame-relevant Wikipedia articles (See SI Appendix 4.5
Table 14) while using a random concatenation of articles across all five
frames as a baseline document. We use SAGE to identify frame-relevant n-grams,
when $n\in[1,3]\\}$. We manually verified the keywords identified by SAGE to
ensure precision and relevance. Keywords/phrases extracted for each frame is
shown in SI Appendix 4.5 Table 15 lists all the keywords/phrases extracted for
each frame. While advances have been made in topic-modeling approaches Blei et
al. (2003); Peinelt et al. (2020); Grootendorst (2022), the stochasticity in
terms of topics detected dictated our choice towards a statistical approach in
Eisenstein et al. (2011) that allowed us to define the frames of interest.

Upon identifying relevant phrases, we identify tweets that contain them. The
statistics of tweets identified using this approach are shown in Appendix 4.5
Table 16. The goal is to automatically identify all words used in association
with these anchors, i.e., words that were used significantly more frequently
to modify frame-relevant terms than general terms. More specifically, these
modifying terms are adjectives, verbs, and nouns that are associated with the
anchor term. We identify them by applying part-of-speech tagging
111https://spacy.io/usage/linguistic-features#pos-tagging and dependency
parsing 222https://spacy.io/api/dependencyparser on all tweets identified to
be frame-relevant.

## Results

The Materials and Methods section provides information on the data and
methodology used in this study, and the Supplementary Information (SI) offers
additional details about materials and methods as well as robustness checks
and validation of the machine learning classifiers. From the set of 57M tweets
collected in Chang et al. (2023), we excluded retweets, replies, and quoted
tweets to focus primarily on original tweets. This left us with roughly 3.6M
original tweets from 1.1M users between January 1, 2022 to January 1, 2023.

We estimate political ideology of each user by analyzing URLs embedded in
users’ original tweets, as done in Rao et al. (2021). The Materials and
Methods section and SI Appendix 4 4.1 offer details on this approach. We also
perform extensive validation of user-level ideology estimates by comparing our
estimates with those produced in previous works Barberá (2015); Wojcieszak et
al. (2024); Rao et al. (2021) (See SI Appendix 44.1.1). In addition, we plot
the estimated the share of conservative users in a state and compare it to
that state’s vote share, finding high agreement. Further, we examine the
differential use of hashtags used for data collection, finding, for example,
that users estimated to be conservative using our approach were roughly 1.5
more likely to use hashtags such as “makeabortionunthinkable” or
“safethebabyhumans,” whereas users estimated to be liberal were more likely to
use hashtags such as “abortionisahumanright” or “forcedbirth” (Refer SI
Appendix 4.1.1 Figure 7). Lastly, we also plot the log odds ratios of the
top-10 most frequently used hashtags, not necessarily abortion related, by
ideology, showing their predicted use by liberals (e.g., “voteblue2022”,
“onev1” and “wtpblue”) and conservatives (e.g., “god”, “trump2024”,
“2000mulesmovie”) (Refer SI Appendix 4.1.1 Figure 7).

Figure 1(a) shows fluctuations in the volume of abortion-related tweets over a
one-year period by users’ ideological leaning. We see notable spikes in
activity, with volume of tweets increasing by almost two orders of magnitude
on May 3rd, 2022, and June 24th, 2022, the dates the draft of the decision was
leaked and of the Supreme Court decision itself. Further, we see an uptick in
engagement on August 3rd, 2022, coinciding with the vote on the Kansas
constitutional amendment. Figure 1(a) also highlights a consistent trend in
which liberals consistently generated a higher volume of abortion-related
tweets compared to conservatives.

Figure 1: Temporal variations of activity and affect of abortion discussions.
(a) Daily number of tweets split by user ideology. Daily fraction of original
tweets posted by liberals (blue) and conservatives (red) that express (b)
anger, (c) fear, (d) sadness and (e) joy, (f) hate speech, (g) toxicity, (h)
obscenity and (i) insult.

### Ideological Differences in Reactions to Key Events

Figure 2: Comparing the distribution of online expressions by ideology.
Boxplot shows the distribution of the daily share of original tweets
expressing anger, fear, sadness, joy, hate speech, toxicity, obscenity, and
insult by liberals and conservatives. Difference between liberals and
conservatives are tested for statistical significance using the Mann-Whitney U
Test with the Bonferroni correction. * indicates significance at $p<0.05$, **
- $p<0.01$, *** - $p<0.001$, **** - $p<0.0001$ and, ns - not-significant.

(a) Ruling Leak - May 3rd 2022

(b) Dobbs Decision - June 24th 2022

(c) Kansas Referendum - August 3rd 2022

(d) Midterm Elections - November 8th 2022

Figure 3: Heatmaps showing the immediate change in affect for liberals and
conservatives. Change is quantified using Interrupted Time Series Analysis.
The values in the heatmap are coefficient of the treatment variable for
liberals and conservatives. Positive coefficients for liberals and
conservatives shows an increase in a given expression immediately following an
event while negative values indicate a decrease. * indicates significance at
$p<0.05$, ** - $p<0.01$, *** - $p<0.001$, **** - $p<0.0001$.

We then examine how emotions, hateful speech, toxicity, obscenity, and insults
in tweets about abortion vary over time and by the political leaning (RQ1)
(Fig.1). The number of original tweets, retweets and replies expressing these
indicators is shown in SI Appendix 4.3 Table 7. In addition, in SI Appendix
4.3.1 Fig. 9, we discuss the relationship between these different indicators.
Furthermore, SI Appendix 4.3 shows detailed statistical results comparing
liberals and conservatives on the mean emotional expression and the prevalence
of hate speech, toxic language, and obscenities in all tweets (see Table 8)
and original tweets see (Table 9).

Overall, conservatives expressed more anger in their tweets about abortion but
also more joy and less fear than liberals (Fig.2). The emotional responses of
the two groups fluctuated over time and especially in the aftermath of key
events, specifically the leak (May 3rd, 2022), the official Dobbs ruling (June
24th, 2022), the Kansas referendum (August 3rd, 2022) and the US midterm
elections (November 8, 2022). Fig. 1a) shows that the periods after the leak
and the Supreme Court ruling were characterized by increases in negative
emotions like anger (Fig. 1b) and fear (Fig. 1c), hate speech (Fig. 1f) and
toxicity (Fig. 1g), and decrease of joy (Fig. 1e).

We use the interrupted time series design to measure emotional reactions to
key events. This analysis enables us to quantify discontinuities in affect in
response to a treatment, i.e., an event, test their significance and
disentangle the heterogeneous effects of users’ ideology Wagner et al. (2002).
We examine changes in trends at a 4-hour resolution over a time period
covering the week before and after the event. We define separate regression
models for liberals and conservatives (see Materials and Methods section). The
heatmaps in Fig. 3(a)-(d) show the coefficients of the treatment variable for
liberals and conservatives, which represent the immediate change in intercept
after an event. Asterisks highlight statistically significant changes in the
intercept. Regression plots highlighting the trends for liberals and
conservatives pre- and post-events are shown in SI Appendix 4.4 Fig. 13.

After the leak, both groups expressed significantly more anger (Fig.3a). The
share of angry tweets by liberals increased by 15%, from 30% to 45%, and only
by 2% among conservatives, from 45% to 47%. These shifts are confirmed in
Fig.3(b). While the increase among conservatives is counter intuitive, it
reflects frustration among conservatives who viewed the leak as an attempt by
liberals to delegitimize the court and intimidate conservative justices. The
rejection of the proposed amendment in Kansas on August 3, 2022 led to a
decline in anger for liberals 42% to 35% in comparison to conservatives (43%
to 39%) ((Fig. 1b) and Fig.3c).

Although liberals expressed more fear overall (4.9% vs 3.8%) overall (Fig.2),
conservatives gradually increased their use of fear after the leak, from 3% to
over 12% after the leak and in the lead up to the official ruling (Fig. 1c). A
review of the tweets suggests that this was due to their worries that the
decision would unleash violent mass protests. Fear decreased after the ruling
for both groups, especially for conservatives from about 10% to under 4% (Figs
1c and 3b).

With regard to sadness, there were no statistically significant differences
between the two groups overall (Fig. 2). Yet, expressions of sadness
fluctuated differently for liberals and conservatives around key events (Fig.
1d). While there was an immediate, significant increase in sadness for
liberals post ruling, from 7% to 9% (Figs. 1d and 3b), the increase in sadness
for conservatives was more gradual from roughly 7% to over 12% by July 9th,
2022. While the increase for liberals can be attributed to their
disappointment at the overruling of Roe vs Wade, the increase in conservative
sadness mainly stems from tweets referring to liberals as “baby killers” or
“demonic liberals,” which were classified as expressing sadness. The peak in
conservative sadness on July 9, 2022 reflects reactions to President Biden’s
executive order to protect access to reproductive health care.

While conservatives express more joy in their abortion-related tweets than
liberals (18.1% versus 14.2%) (Fig. 2), tweets expressing joy declined on both
sides over the analyzed time period (Fig. 1e). Expression of joy declined from
20% to roughly 10% for both groups immediately after the leak of the draft
ruling, as indicated by statistically significant coefficients for both the
groups in Fig. 3(a). A review of the tweets also suggests that conservatives
did not greet the repeal of Roe v. Wade as a victory, even though this was
their long-term goal. We validate the results for emotions using sentiment
analysis. We quantify the overall sentiment expressed by liberals and
conservatives during this time period and present the results in SI Appendix
4.3.5.

When it comes to problematic speech, i.e., hate speech, toxicity, insults, and
obscenity, we observe substantial ideological asymmetries. In the aggregate,
hate speech is more prevalent among conservatives than liberals (5% versus 2%)
(Fig 2). The use of hate speech further increased among conservatives after
the leak and the Dobbs decision (Fig. 3a,b) and (Fig 1f). On the day of the
leak, conservative usage of hateful speech significantly increased from 2% of
their tweets to roughly 6%, and to almost 10% after the official verdict.
These increases for conservatives are statistically significant as seen in
Figs.3(a) and 3(b). An examination of the tweets suggests that these increases
are due to the prevalence of misogynistic and xenophobic sentiments among this
group. One prominent surge in mid-July was directed at immigrants, following
news of a 10 year old girl from Ohio who was allegedly raped by an immigrant
and who was seeking abortion in Indianapolis. Another notable surge in hateful
speech occurred in the lead up to the 2022 midterm elections. Although
conservative hate speech temporary declined following the Kansas referendum
from 7% to under 2%, it again increased before the 2022 midterm elections
culminating close to over 10% at the end of December 2022. In contrast, the
incidence of hate speech among liberals remains consistently low, below 5%
throughout the year and with no significant spikes.

Similarly, conservative users were more toxic overall than liberals (10%
versus 8%) (Fig. 2). Yet both liberals and conservatives sharply increased
their use of toxic language following the leak (from 7% to 11% and from 8% to
15%, respectively) and even more so after the decision, from 7% to 15% and
from 7% to 16% respectively (Figs.3a,b). Toxicity among liberals decreased
shortly afterwards in both cases (Fig. 1g), while it remained stable among
conservatives. The increases in toxic speech is seen to be statistically
significant through interrupted time series analysis in Figs.3(a) and 3(b). In
the lead up to the midterm elections, conservatives remain more toxic overall
in comparison to liberals.

In contrast, liberals generally use more obscenities and insults than
conservatives (obscenities 3.5% versus 2.3%, insults 2% versus 1%) (Fig 2).
Liberal expression of obscenities marginally increases from 4% to 5.5% after
the leak and more substantially from 3% to 6% after the official ruling (Fig
1h and 3a,b). Liberals substantially increased their use of insults on the day
of the leak from 0.6% to 3% of all original tweets as opposed to a marginal
increase for conservatives from 1.6% to 1.7%. Both liberals and conservatives
increased their use of insults on the day of the Dobbs ruling, from 1% to 4%
(Fig 1i). The fact that obscenities and insults fluctuate in parallel ways
among both liberals and conservatives reflects the intensity of online
debates.In addition, Fig. 10 in SI Appendix 4.3.2 shows the share of all
liberal or conservative tweets expressing anger, fear, sadness and joy,
hateful speech, toxicity, obscenity and insults.

Figure 4: Temporal and emotional analysis of frames in the abortion debate.
Daily share of original tweets posted by liberals (blue) and conservatives
(red) containing frames about (a) religion, (b) bodily autonomy, (c) fetal
rights, (d) women’s health and (f) and (e) abortion exceptions. Corresponding
word clouds highlight the semantic contexts in which these frames are
discussed by liberals and conservatives. The intensity of color denotes which
group is more likely to use a phrase (shades of red for conservatives and
shades of blue for liberals) and text size denotes the likelihood of usage.
Scatter-plots shown compares the share of liberal and conservative tweets
expressing anger, fear, sadness, joy, hate, toxicity, obscenities, and insults
for each frame. The diagonal represents equal expression of the emotions from
both groups with values above the diagonal indicating increased conservative
expression and values below indicating higher liberal expression. We apply log
transformation on the scatter-plots’ axes to account for better visualization.

### Frames

To better understand how liberals and conservatives discuss abortion, we
extract five frames previously identified as relevant to abortion debate in
the U.S.: Marquis (1989); Harris and Mills (1985); Cohen (2015); Greasley
(2017); Mackenzie (2020); Pew (2022); Hartig (2022): religious beliefs, bodily
autonomy, fetal personhood, women’s health, and abortion exceptions (see
Materials and Methods and SI Appendix 4.5).

We examine the use of these frames by each group and also the context in which
these frames are used. Figure 12(a) shows the log-odds ratio of which group —
liberals or conservatives — is more likely to use each frame when tweeting
about abortion, showing that conservatives were more likely to use religious
and fetal personhood frames and liberals were more likely to frame their
discourse around bodily autonomy and women’s reproductive health.
Conversations around exceptions to the abortion ban were also marginally more
liberal.

Figures 4(a)-(e), shed light on the use of these five frames over time by both
liberal and conservatives. We see a substantial decline in the prevalence of
the women’s health frame in liberal discourse after the leak, from 41% to
merely 10%. The use of the bodily autonomy frame became more prevalent
instead: the use of this frame increased among liberals from 18% to 32%
following the Kansas referendum. In contrast, conservatives reacted to this
event with an increase use of fetal personhood frame, which climbed from 12%
to 40%.

In addition, we employ simple linguistic techniques of part-of speech tagging
and dependency parsing to extract the context in which liberals and
conservatives use these five frames. Word clouds in Fig. 4(a)-(e) highlight
the diverging perspectives of the two groups. In religious discourse related
to abortion (see Fig. 4a), liberals use the term “evangelical,” while
conservatives use “catholic.” Furthermore, liberal messaging adopts a critical
tone, utilizing phrases such as “secretive christian,”“right-wing
evangelical,” or “molestation church,” among others. In contrast,
conservatives employ phrases like “prioritize the church,” “advocates prayer,”
or “way of the church.” When discussing bodily autonomy (Fig. 4b), liberals
rely on pro-choice messaging (e.g., “pro-choice march” or “total pro-choice”)
while conservatives actively reject the pro-choice stance through phrases such
as “denying pro-choice” or “change pro-choice”.

The anti-abortion stance of conservatives is further seen in the context of
fetal personhood frame (Fig. 4c), where they use phrases such as - “left
infanticide”, “petition for prolife”, “defenceless unborn”, “gorgeous babies”,
or “patriots for unborn.” In contrast, liberals remain critical of the
personhood argument, using phrases such as - “condemn personhood,” “lose
personhood,” “reject personhood.” or “prolife anti woman.” Furthermore,
conservatives rely on words such as “baby” and “unborn,” which are not used in
liberal framing Simon and Jerit (2007). In conversations around general
reproductive care and contraception (Fig. 4d), liberals use phrases such as
“protection for contraception,” “voted for contraception,” “legalize
contraception,”or “approval for mifepristone,” among other mentions of
reproductive laws. In contrast, conservative discussion of the women’s health
frame relies on phrases such as “evil contraception”, “sterilization camps,”
or “block progesterone.” Similarly, liberals and conservatives discuss fetal
viability (Fig. 4e), exceptions to abortion restrictions, and gestational
limits in divergent contexts. Liberal discourse reflects criticism of a total
ban on abortion access with a frequent use of phrases such as “fetal
abnormalities,” “multiple abnormalities,” “cruel rapist,” or “exempting rape.”
In contrast, conservatives question these exemptions using phrases such
as“alleged rapist”, “fabricated rape” or “conceived out of rape”, as well as
“week old viable” or “preterm babies.” SI Appendix 4.5 offers an extended list
of the words and phrases used for each of the five frames.

Lastly, we examine whether and the extent to which there are ideological
asymmetries in the expression of anger, fear, sadness, joy, hate speech,
toxicity, obscenities, and insults in the discussions of the five frames.

The diagonal represents equal expression of anger, fear, sadness, joy, hate
speech, toxicity, insults, and obscenities by both groups. Values above the
diagonal indicate a heightened presence of these expressions in conservative
tweets, whereas values below the diagonal indicate a greater presence of these
expressions in liberal tweets. The scatterplots in Figs. 4(a-e) show that
liberals express more negative attitudes when discussing the religion and
fetal personhood frames, whereas conservatives express more negative attitudes
when discussing women’s health and bodily autonomy. Interestingly, discussions
of abortion exceptions elicit the highest levels of anger, fear, hate speech,
and toxicity from both groups, but especially for liberals, suggesting the
potency of the issue to liberals.

Utilizing inferred geolocations (see Materials and Methods section), we assign
Twitter users to specific states and quantify the proportion of tweets
mentioning each frame that originate in each state. Fig. 12(b)-(f) visualizes
state-level distribution of the proportion of tweets discussing these five
frames. Discourse associated with religion and fetal personhood, used by
conservatives (see Fig. 12(a)), is substantially more frequent in the South.
In turn, discussions centered on bodily autonomy and women’s health, primarily
favored by liberals (Fig. 12a)), are more pronounced in the Northeast and the
West. Intriguingly, users in North Carolina heavily rely on both fetal
personhood and bodily autonomy frames, potentially indicating the divides in
public opinion in that state.

### Discussion

This study examined how online discussion around abortion access fluctuated
over the period of twelve months that encompass the decision by the Supreme
Court of the United States (SCOTUS) to overturn the Roe vs. Wade. This
decision offers a unique opportunity to explore online conversations during
the times of a major societal upheaval. Leveraging an extensive datasetChang
et al. (2023), we investigate the fluctuations in the expressions of anger,
fear, sadness, and joy, of problematic speech (i.e., hateful speech, toxicity,
insults, and obscenity), and also of the use of key frames central to the
abortion debate. We tested these expressions along the ideological divide
between liberals and conservatives, a divide that has widened in recent years,
with Democrats increasingly becoming pro-abortion and Republicans opposed to
it Hartig (2022); Saad (2022).

We offer three key findings. First, anger was the dominant emotion in
abortion-related tweets, a finding that underscores the contentious nature of
the abortion rights debate and the tensions following the overturn of Roe v
Wade. Emotional expressions differed between liberals and conservatives in
response to key events. Anger surged immediately after the leak, especially
among conservatives, who perceived it as an attempt to delegitimize the court
and intimidate conservative justices. While the expressions of anger remained
stable among conservatives, liberals’ expression of anger increased after the
leak and decreased after the Kansas referendum. Fear increased significantly
for conservatives after the leak, an increase driven by concerns about
potential left-wing protests. Both liberals and conservatives experienced
peaks in sadness after the official verdict. Liberals were disappointed by the
overruling of Roe vs. Wade, while conservatives were displeased with President
Biden’s executive order protecting abortion access. These fluctuations,
however, were temporary and pivotal events did not bring about lasting
emotional shifts. Instead, emotions oscillated, reflecting the collective mood
of each group in response to the various events analyzed, and reverted to the
initial equilibrium shortly after.

Second, we observe significant ideological differences in the use of hate
speech. We find that conservatives resorted to hateful rhetoric more than
liberals. Yet, liberals were especially likely to use obscenities and insults,
especially on the days the ruling was leaked and after the Dobbs decision. In
turn, toxic language sharply increased among both groups following the leak
and especially after the SCOTUS ruling.

These two findings suggest potential emotional contagion and the fact that
liberals and conservatives mobilize and trigger each other in their online
discourse. The utilization of hateful rhetoric, toxic language, and the
incorporation of obscenities consistently diverge between these groups
throughout the entire period.

Third, we shed light on how online discussions about abortion are driven by
and reflect key frames: religion, bodily autonomy, fetal personhood, and
women’s health and exceptions Marquis (1989); Simon and Jerit (2007); Pew
(2012); Cohen (2015); Greasley (2017); Mackenzie (2020); Pew (2022); Hartig
(2022); Norrander and Wilcox (2023). We show that conservatives tend to employ
religious and fetal personhood frames, while liberals emphasize women’s health
and bodily autonomy. Although these patterns may be expected, ours is the
first study to show the use and the prevalence of these patterns using online
behavioral data from millions of partisan Twitter users in the United States.
Furthermore, we show that the frames favored by one side provoke strong
negative reactions from the other: liberals express more anger and toxicity
when discussing religion, while conservatives use more toxic language and hate
speech when addressing women’s health. Interestingly, abortion exceptions,
particularly for cases of rape or incest, trigger powerful negative reactions
from both sides, underscoring the issue’s centrality in the abortion debate.
Furthermore, geographical variations closely align with ideological
asymmetries. The increased expression of anger, fear, and toxic rhetoric among
liberals regarding frames central to conservative abortion discourse,
paralleled by a corresponding increase in negative emotions among
conservatives when addressing issues pivotal to liberal discourse underscores
the stark divisions between these two ideological groups.

Furthermore, examining the time series of frames over the period of one year,
we show that important events related to the abortion debate, namely the leak
and the Dobbs ruling, did not generate substantial fluctuations in the used
frames. Instead, it was the Kansas referendum that changed the discussions and
led to fluctuations in the used frames. This finding indicates the stability
and persistance of these key five frames in partisan discourse around abortion
and also may suggest that more unexpected events (i.e., the Kansas referendum)
may have larger impacts on the shifts in online discourse.

Our research throws light on the impact of the Dobbs v. Jackson Women’s Health
Organization decision on the abortion rights debate, exposing the intricate
interplay between collective emotions and evolving narratives in ideological
cohorts. Analyzing a vast dataset of over over 57 million tweets from more
than 5 million users over a year, we unveil a stark divide in emotional
responses, hate speech, toxic language, and ideological perspectives among
liberals and conservatives. Our findings not only underscore the influence of
significant events on these discussions but also shed light on the evolving
public sentiment. Furthermore, our study identifies frames that resonate
strongly with one side while evoking vehement opposition from the other. This
research offers a comprehensive perspective on how online discourse not only
reflects but actively shapes societal dialogues on contentious issues,
underscoring the dynamic and transformative nature of the abortion debate in
contemporary America.

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## Appendix

##  3 Data Collection

###  3.1 Keyword Selection

Prior to collecting the tweets, we identified a set of keywords that are
representative of the discourse about abortion in the United States by
different sides, as detailed below. These keywords are listed in Table 1. We
followed a three-pronged data collection strategy. We gathered tweets in real-
time from June 25, 2022 to January 6, 2023. In addition, we leveraged Twitter
Academic API’s Full-Archive Search
333https://developer.twitter.com/en/docs/twitter-api/tweets/search/api-
reference/get-tweets-search-all to collect historical tweets from January 1,
2022 to June 24, 2022. In order to verify coverage, we use the count end-point
from the Twitter API 444https://developer.twitter.com/en/docs/twitter-
api/tweets/counts/api-reference/get-tweets-counts-all to compare the number of
tweets in our collection versus the number of tweets identified on Twitter. We
use the Full-Archive Search endpoint to recollect the data if the total number
of tweets per day in our dataset is less than the number of tweets identified
on Twitter. Twitter provides a language field in its tweet object using which
we restrict our collection to English tweets. In all, we have 57,540,676
tweets made by 5,426,555 unique users.

Table 1: List of keywords Category |  Keywords
---|---
Neutral |  #Abortion, #roevswade, #Roeverturned, #roevwade, abortion, roe vs wade, roe v wade, roe overturned
Pro-choice |  #roevwadeprotest, roe v wade protest, pro choice, pro-choice, #prochoice, #forcedbirth, forced birth, #AbortionRightsAreHumanRights, abortion rights Are Human Rights, #MyBodyMyChoice, My Body My Choice, #AbortionisHealthcare, abortion is healthcare, AbortionIsAHumanRight, abortion is a human right, ReproductiveHealth, Reproductive Health, AbortionRights, abortion rights
Pro-life |  #prolife, pro life, pro-life, #EndAbortion, end abortion, #AbortionIsMurder, Abortion Is Murder, #LifeIsAHumanRight, Life Is A Human Right, #ChooseLife, Choose Life, #SaveTheBabyHumans, Save The Baby Humans, #ValueLife, Value Life, #RescueThePreborn, Rescue The Preborn, #EndRoeVWade, End Roe V Wade, #LifeMatters, Life Matters, #MakeAbortionUnthinkable, make abortion unthinkable, #LiveActionAmbassador, Live Action Ambassador, Abortion Is Not A Right, #AbortionIsNotARight, #AbortionIsRacist, Abortion is racist, anti life, #antilife

###  3.2 Keyword Collection

To identify the set of relevant keywords to serve as search terms for the
Full-Archive Search endpoint of Twitter Academic API, we conducted an
exploratory data collection of abortion-focused discussions reflecting
different political stances. The identified keywords represent both phrases
and hashtags. We then build a systematic data collection pipeline.

####  3.2.1 Hashtag Sampling

We identified seed hashtags reflecting different sides of the abortion debate:
#roevwade (neutral), #prochoice (pro-abortion), and #prolife (anti-abortion).
We collected tweets mentioning these hashtags using Twitter
API.555https://developer.twitter.com/en/docs/twitter-api/tweets/search/api-
reference/get-tweets-search-recent We identified hashtags that frequently co-
occur with the seed hashtags within tweets. For #prochoice, these include
#roevwadeprotest, #forcedbirth, #AbortionRightsAreHumanRights,
#MyBodyMyChoice, #abortionishealthcare, #abortionisahumanright,
#ReproductiveHealth, and #abortionrights; for #prolife, these include
#EndAbortion, #AbortionIsMurder, #LifeIsAHumanRight, #ChooseLife. #valueLife,
#rescuethepreborn, #endroevwade, #lifematters, #antilife,
#makeabortionunthinkable, #liveActionAmbassador,#catholic,
#AbortionIsNotARight, and #Abortionisracist; and for #roevswade these include
#Abortion, #Roeverturned.

####  3.2.2 Creating keywords

Not all Twitter users include hashtags in their tweets. Therefore, we create a
list of keywords based on the above hashtags by simply removing # from the
hashtags. We break up composite hashtags into phrases (e.g.,
#makeabortionunthinable becomes “make abortion unthinkable”). The final set of
hashtags and phrases shown in Table 1 forms the search terms in our query.
Specifically, we create a search query connecting keywords with the ’OR’
operator, which tells the search endpoint to return tweets that have at least
one keyword in it. The search is not case-sensitive. We removed non-English
tweets from the dataset.

##  4 Classifiers

We used state-of-the-art statistical and language models to classify users’
political ideology, emotions, hate speech, toxicity, insults, and obscenities
in the text of the tweets Rao et al. [2021], Alhuzali and Ananiadou [2021],
Hanu and Unitary team [2020], Barbieri et al. [2022]. Below, we present the
additional details on the models and the validations we executed to ensure
reliability of predictions.

###  4.1 User Ideology

We estimate each individual’s ideological leaning as the weighted average of
the political bias scores of the URLs they share. These bias scores are
determined by Media Bias-Fact Check (MBFC) Check , which scores more than 3K
Pay-Level Domains (PLDs) as Left/Hardline Liberal (score=$0$), Left-Center
($0.25$), Least-Biased/Center ($0.5$), Right-Center ($0.75$), Right/Hardline
Conservative ($1$). We use tldextract 666https://github.com/john-
kurkowski/tldextract to extract pay-level domains from URLs in tweets. We
filter out tweets and retweets containing pay-level domains that are not
categorized under either of the two ideological polarities of interest (Table
2).

Table 2: Curated pay-level domains and their polarity scores along political and factual dimensions. For the political dimension, $\\{0,0.25,0.5,0.75,1\\}$ represents Left, Center Left, Center/Unbiased, Center Right and Right sources respectively. Dimension |  Polarity |  Pay-Level Domains
---|---|---
Politics |  Left $(0)$ |  cnn.com, huffpost.com, dailybeast.com,$\ldots$ (350+ PLD s)
Center-Left $(0.25)$ |  aljazeera.com, independent.co.uk, lincolnproject.us $\ldots$ (500+ PLDs)
Center $(0.5)$ |  gallup.com, pewresearch.co.uk, wikipedia.com $\ldots$ (500+ PLDs)
Center-Right $(0.75)$ |  bostonherald.com, chicagotribune.com, wsj.com $\ldots$ (250+ PLDs)
Right $(1)$ |  foxnews.com, gppusa.com, thenationalherald.com $\ldots$ (250+ PLDs)

The model described in Rao et al. [2021] works in two passes. In the first
pass, we identify the ideology of users who embed URLs from sources/domains
with known ideological leaning in their tweets. This is done by computing the
weighted-average of domain scores of the URLs as provided by Media Bias-Fact
Check Check . More specifically, we calculate user $u$’s scores along the
ideological $p_{l}(u)$ dimension using Eq. 1. We denote the set of pay-level
domains shared by user $u$ as $D(u)$. These include only the domains appearing
in $u$’s original tweets (and not retweets). $\Pi(d)$ returns the ideological
leaning of each domain $d$ as provided in Check .

$p_{l}(u)=\frac{1}{|D(u)|}\sum_{d\in D(u)}\Pi(d)$ (1)

These scores are then binarized with a threshold of $\leq 0.4$ for liberals
($0$) and $\geq 0.6$ for conservatives ($+1$) and are used to train a text-
based classifier to predict user ideology in the second pass.

The first pass identifies ideology for a smaller subset of users in the
dataset as not all users embed their tweets with URLs to external sources. In
the second pass, the approach in Rao et al. [2021] leverages a fastText
embedding model pre-trained on Twitter data Pagliardini et al. [2018] to
generate embeddings for user’s tweets over time. These embeddings serve as
features to train a Logistic Regression classifier to predict the binarized
ideological leanings for all users, including users who have not shared URLs
from domains in the MBFC. We categorized users as liberal or conservative by
binarizing the output of logistic regression using threshold of 0.5. In all,
we were able to label the ideology of all 5.4M users in our data. The logistic
regression model achieves a F1 score of 86% (93% accuracy, 88% precision, 84%
recall) on a held out test-set from the first pass. Given that this classifier
was binarized, identifying moderate users was not possible. Out of the
$5,426,555$ users in our dataset, $4,188,214$ (77%) were labeled liberal and
$1,238,341$ (23%) were labeled conservative. Of the $1,100,572$ users who
share original content (not retweets, replies or quoted tweets), $867,015$
(79%) of them are liberal and $233,557$ (21%) are conservative. Liberal users
were also found to be more active compared to conservative users in our
dataset with a $11.60$ tweets per user as opposed to $7.22$ for conservatives
(see Table. 3).

(a) Total number of tweets by user partisanship including retweet, original
tweet, quoted tweets and replies.

(b) Total number of original tweets by user partisanship.

Figure 5: Total number of tweets by user partisanship.

Figure 5 shows the total number of tweets disaggregated by user
partisanship.777All time series plots use exponential smoothing.

| Liberals | Conservatives | Total
---|---|---|---
Tweets | 48,589,545 | 8,951,131 | 57,540,676
Users | 4,188,214 | 1,238,341 | 5,426,555
Original Tweets | 2,839,513 | 706,552 | 3,546,065
Retweets | 39,410,734 | 6,050,761 | 45,561,495
Replies | 5,610,747 | 1,968,619 | 7,579,366
Quoted Tweets | 661,551 | 192,199 | 853,750
Table 3: Tweet Statistics

####  4.1.1 Validation

(a) Share of conservatives Twitter users by state

(b) Percentage of votes for President Donald Trump in the 2020 Presidential
election.

(c) Relationship between Twitter ideology estimates and Trump vote share.

Figure 6: Geoplots in (a-b) showing the share of conservative Twitter users
estimated using our ideology detection method and Trump vote share for each
state. Z-score normalization is applied to both plots. States with higher
z-scores tend to be more conservative than ones with lower z-scores. Darker
shades of red represent higher share of users being conservative while darker
shades of blue denotes higher liberal share. (c) shows the correlation
(Pearson’s $r=0.79;(p<0.0001)$) between Trump vote share and share of
conservatives users on twitter per state in our data.

(a) Prominent anchor hashtags used by liberals and conservatives.

(b) Prominent hashtags (from all hashtags) used by liberals and conservatives

Figure 7: Log-Odds Ratio of Hashtags by Ideology. (a) shows the log-odds for hashtags used in data collection (anchor hashtags). Darker shades of red indicate that the hashtag is more likely to be used in conservative discussions whereas darker shades of blue indicate higher hashtag usage amongst liberals. Prominent anti-abortion slogans are more common in conservative discourse and pro-abortion hashtags are found predominantly in liberal discourse. (b) highlights the-10 hashtags (by log-odds-ratio) for liberals and conservatives. Table 4: Comparing the accuracy and Jaccard overlap between user ideology estimates in our work and previous works Barberá [2015], Wojcieszak et al. [2024] on a sample of 252,492 Twitter users common to all three methods. Metric | Ideal-Point Estimate Barberá [2015] | Elite Average Wojcieszak et al. [2024]
---|---|---
Accuracy | 0.81 | 0.83
Fleiss Kappa | 0.49 | 0.52
Jaccard Overlap | 0.83 | 0.87

In order to assess the validity of our ideology estimates, we first compare
them, at the state level, against percentage of votes for President Donald
Trump in the 2020 Presidential elections. More specifically, relying on the
geolocation inference technique discussed previously Dredze et al. [2013], we
isolate users to respective states. We then calculate the proportion of each
state’s users who are conservative and assess its similarity to the state’s
Trump vote share. Figs. 6(a) and (b) show the share of conservative Twitter
users in each state and the percentage of votes for Trump in the 2020 Federal
elections. The ideology estimates using the two approaches are considerably
similar. The strong state-level correlation (Pearson’s $r=0.79;(p<0.0001)$)
between these two measures, shown in Fig. 6(c), also affirms the validity of
this approach.

In addition, we perform further validation of the user ideology estimates by
comparing our estimation approach to the results of prior methods that are
based on 1) the structure of follow interactions Wojcieszak et al. [2024],
Barberá [2015] and 2) domain sharing behaviors Rao et al. [2021]. More
specifically, Barberá [2015] infers user ideology based on the Twitter
accounts the user follows. The underlying assumption is that users follow
political figures with congruent views. McCabe et al. [2022] applied the same
methodology using an updated list of political figures, and Wojcieszak et al.
[2024] used it to infer the ideology of a set of Twitter users. In contrast,
Ref. Rao et al. [2021] relied on content in the form of URLs and tweet text to
draw estimations on user political leaning. Note that neither of these
alternative methods applies to all users in our dataset. We compare the
ideology estimated by the different methods on a subset of 252,492 users that
overlap across the three methods Barberá [2015], Wojcieszak et al. [2024], Rao
et al. [2021] in Table 4. The methods discussed in Barberá [2015], Wojcieszak
et al. [2024] rely on expensive to collect follow relationships and curating
estimates using these approaches for all 5.5M users can be cumbersome. Relying
on features in text offers greater flexibility. In addition to this, the
estimates of user ideology used in Rao et al. [2021] agree highly with
previous methods. In comparison to Wojcieszak et al. [2024], our estimates
were 81% accurate and in comparison to the ideal-point estimates in Wojcieszak
et al. [2024] and 83% when compared to Barberá [2015] (Table 4). Additionally,
using Jaccard scores as a measure of similarity, our ideology classification
has 98% Jaccard similarity to Wojcieszak et al. [2024] and 83% to Barberá
[2015] (Table 4).

Table 5: User ideology comparison on a sample of 1.4 Twitter users labeled by all methods. Ideology |  Ideal-Point EstimateBarberá [2015] |  Elite Average Wojcieszak et al. [2024] |  URL+Text EmbeddingRao et al. [2021]
---|---|---|---
Liberal |  72% |  75% |  79%
Conservative |  28% |  25% |  21%

We provide further validation by presenting figures of the total number of
tweets over time by partisanship across different user ideology methodologies
- neighborhood elite averages (Ref. Wojcieszak et al. [2024], ideal point
estimates Barberá [2015] and URL-text features Rao et al. [2021]) in figure 8.
We can clearly see that the highs and lows in the time series for various
ideological methods closely mirror each other.

(a) Ideology estimation using PLDs and Tweet text (Rao et al. [2021])

(b) Ideal Point Estimates Barberá [2015]

(c) Average neighborhood elite score Wojcieszak et al. [2024]

Figure 8: The total number of tweets over time by partisanship across
different user ideology methodologies - (a) our estimation method based on Rao
et al. [2021], (b) average of elites’ ideology estimates in neighborhood
(Wojcieszak et al. [2024]) and, (c) ideal point estimates discussed in Barberá
[2015]. We observe that the peaks and valleys in time series across different
ideology methods are near identical.

As an additional validation, we assess the prominent hashtags used by liberals
and conservatives (Fig. 7. We find that conservative users were roughly 1.5
more likely to use hashtags such as “makeabortionunthinkable” or
“safethebabyhumans.” In turn, liberal users were more likely to use hashtags
such as “abortionisaumanright” or “forcedbirth.” By plotting the log odds
ratios of the top-10 most frequently used hashtags, we find that some
hashtags, although not necessarily abortion related, express ideology, as
indicated by the liberals’ usage of hashtags such as “voteblue2022”, “onev1”
and “wtpblue” in comparison to “god”, “trump2024”, “2000mulesmovie”, used by
conservatives. We provide further validation by presenting figures of the
total number of tweets over time by partisanship across different user
ideology methodologies in Fig. 8. We can clearly see that the the peaks in the
time series for various ideological methods closely mirror each other.

In conclusion, our estimates of user ideology are highly accurate, as
determined by validating them with estimates from alternate methods and using
the text of the tweets, and can be applied successfully to infer the ideology
of most users in our dataset.

###  4.2 Geolocation

We utilized Carmen Dredze et al. [2013] 888https://github.com/mdredze/carmen-
python, a geo-location identification technique for Twitter data to assign
tweets to locations within the U.S. This method leverages tweet metadata,
including “place” and “coordinates” objects that encode location information,
such as country, city, and geographic coordinates. Additionally, the technique
uses mentions of locations in a user’s bio on Twitter to infer their location.
A manual review confirmed that this approach was effective in identifying a
user’s home state. Among the 57M tweets in our dataset, state-level locations
were inferred for about 15M tweets. California, New York, Florida and Texas
together accounted for roughly 39% of all tweets from the US. California, in
particular, accounted for the most activity (14%) among all states in the US
as detailed in Table 6.

Table 6: Total number of tweets identified by the state. State | Count | Percent
---|---|---
California | 1913971 | 14.82
New York | 1027713 | 7.96
Florida | 1013958 | 7.85
Texas | 1003907 | 7.77
Ohio | 552474 | 4.28
Pennsylvania | 527912 | 4.09
Illinois | 461500 | 3.57
Michigan | 414321 | 3.21
Arizona | 397988 | 3.08
Georgia | 384480 | 2.98
District of Columbia | 349089 | 2.70
Virginia | 345393 | 2.67
New Jersey | 300889 | 2.33
Oregon | 282594 | 2.19
Minnesota | 263161 | 2.04
North Carolina | 260194 | 2.02
Washington | 256440 | 1.99
Massachusetts | 251572 | 1.95
Tennessee | 228870 | 1.77
Maryland | 225423 | 1.75
Colorado | 220164 | 1.71
Wisconsin | 206368 | 1.60
Missouri | 195743 | 1.52
South Carolina | 159713 | 1.24
Nevada | 156149 | 1.21
Indiana | 152024 | 1.18
Louisiana | 144701 | 1.12
Connecticut | 124563 | 0.96
Alabama | 95774 | 0.74
Kentucky | 91538 | 0.71
Oklahoma | 87046 | 0.67
New Hampshire | 72467 | 0.56
Kansas | 72392 | 0.56
New Mexico | 71068 | 0.55
Iowa | 70547 | 0.55
Utah | 63257 | 0.49
Nebraska | 50987 | 0.39
Hawaii | 49066 | 0.38
Arkansas | 47666 | 0.37
West Virginia | 47434 | 0.37
Idaho | 42808 | 0.33
Washington, D.C. | 34856 | 0.27
Rhode Island | 32603 | 0.25
Mississippi | 31179 | 0.24
Montana | 29910 | 0.23
Alaska | 25908 | 0.20
Vermont | 20725 | 0.16
South Dakota | 13703 | 0.11
Maine | 11177 | 0.09
North Dakota | 10954 | 0.08
Wyoming | 9887 | 0.08
Delaware | 8110 | 0.06

###  4.3 Tweet-Level Predictions

Using state-of-the-art classifiers we identify anger, fear, sadness, joy,
hateful and toxic speech, obscenities and insults in the content of the
tweets. Statistics on the tweets are shown in Table 7.

Table 7: The total number of tweets and original tweets expressing anger, fear, sadness, joy, hate speech, toxicity, obscenities and insults. Affect | #All Tweets | #Original Tweets
---|---|---
Anger | 27,899,673 | 1,564,102
Fear | 2,577,870 | 153,749
Sadness | 4,240,726 | 300,601
Joy | 3,754,015 | 289,424
Hate Speech | 1,359,485 | 151,090
Toxicity | 5.837,417 | 410,759
Obscenity | 2,154,141 | 186,208
Insult | 1,157,443 | 100,749

####  4.3.1 Correlation

(a) Liberal

(b) Conservative

Figure 9: Correlations of time-series of emotions, hate speech, toxicity,
insults, and obscenities for (a) liberals and (b) conservatives. The
correlations are calculated on time-series data composed of the share of
liberal/conservative tweets expressing a certain affect.

We then proceeded to compute the correlations among the temporal patterns of
emotions, hate speech, obscenity, and insult, focusing on individuals with
liberal and conservative affiliations (see Fig. 9). Upon examining these
correlations, we uncovered intriguing dissimilarities between the two groups.

Feature Name |  Liberal |  Conservative |  $p$-value
---|---|---|---
Mean Fraction of Anger |  0.434 |  0.416 |
Mean Fraction of Fear |  0.048 |  0.037 |  ****
Mean Fraction of Sadness |  0.072 |  0.079 |  ****
Mean Fraction of Joy |  0.110 |  0.150 |  ****
Mean Fraction of Hate Speech |  0.020 |  0.041 |  ****
Mean Fraction of Toxicity |  0.085 |  0.107 |  ****
Mean Fraction of Obscene |  0.032 |  0.011 |  ****
Mean Fraction of Insult |  0.015 |  0.010 |  **
Mean Fraction of Positive Sentiment |  0.089 |  0.096 |
Mean Fraction of Negative Sentiment |  0.634 |  0.605 |  ***
Mean Fraction of Neutral Sentiment |  0.276 |  0.299 |  ***
Fraction of Original Tweets |  0.066 |  0.091 |  ****
Fraction of Quoted Tweets |  0.013 |  0.021 |  ****
Fraction of Retweets |  0.818 |  0.687 |  ****
Fraction of Replies |  0.115 |  0.219 |  ****
Total Number of Unique Partisan Users |  4,882,120 |  1,421,454 |
Total Number of Tweets |  48,589,545 |  8,951,131 |
Table 8: Comparing statistics by User Partisanship for all tweets. We use **** to denote p-value <0.0001, *** for p-value < 0.001, ** for p-value < 0.01, and * for p-value<0.05. The significance was measured using the non-parametric Mann-Whitney U Test by comparing the time series between Liberals and Conservatives. The time series is comprised of average daily fraction of all tweets expressing an affect/indicator. Feature Name |  Liberal |  Conservative |  $p$-value
---|---|---|---
Mean Fraction of Anger |  0.346 |  0.366 |  ***
Mean Fraction of Fear |  0.041 |  0.033 |  ****
Mean Fraction of Sadness |  0.073 |  0.075 |
Mean Fraction of Joy |  0.142 |  0.182 |  ****
Mean Fraction of Hate Speech |  0.026 |  0.047 |  ****
Mean Fraction of Toxicity |  0.082 |  0.096 |  ****
Mean Fraction of Severe Toxicity |  0.0007 |  0.0005 |  ****
Mean Fraction of Obscene |  0.033 |  0.023 |  ****
Mean Fraction of Threat |  0.001 |  0.003 |  ****
Mean Fraction of Insult |  0.021 |  0.014 |  ****
Mean Fraction of Positive Sentiment |  0.086 |  0.101 |  *
Mean Fraction of Negative Sentiment |  0.483 |  0.518 |  ****
Mean Fraction of Neutral Sentiment |  0.429 |  0.379 |  ****
Total Number of Unique Partisan Users |  870,145 |  234,699 |
Table 9: Comparing statistics by User Partisanship for original tweets. We use **** to denote p-value <0.0001, *** for p-value < 0.001, ** for p-value < 0.01, and * for p-value<0.05. The significance was measured using the non-parametric Mann-Whitney U Test by comparing the time series between Liberals and Conservatives. The time series is comprised of average daily fraction of original tweets expressing an affect/indicator. Table 10: Performance of Emotion Detection Classifier Emotion | Accuracy | Precision | Recall | F1
---|---|---|---|---
Anger | 0.829 | 0.657 | 0.900 | 0.795
Fear | 0.956 | 0.700 | 0.618 | 0.656
Joy | 0.955 | 0.608 | 0.925 | 0.733
Sadness | 0.941 | 0.400 | 0.744 | 0.520

Anger exhibited a strong association with fear among conservatives, whereas
this connection was not evident among liberals. A surge in the expression of
anger on a given day displayed strong links with heightened sadness, hate
speech, and toxicity for conservatives. In contrast, liberals demonstrated a
more pronounced tendency to employ obscenities and insults in conjunction with
anger. Furthermore, fear among conservatives exhibited heightened correlations
with sadness and an increased proclivity toward employing hate speech compared
to liberals. The expression of sadness by liberals concurrently manifested an
elevated use of hate speech, a phenomenon not observed among conservatives.
Additionally, liberals exhibited a proclivity for utilizing more insults and
obscenities when experiencing sadness, while conservatives displayed a higher
incidence of toxic behavior. Moreover, when liberals exhibited heightened
toxicity, they also displayed a greater propensity for hate speech, insults,
and obscenities compared to conservatives.

####  4.3.2 Emotions

For our emotion detection task, we employed SpanEmo Alhuzali and Ananiadou
[2021], a state-of-the-art multi-label emotion detection model. This model was
fine-tuned using the SemEval 2018 Task 1e-c dataset Mohammad et al. [2018].
SpanEmo, using a transformer-based architecture. It surpasses previous methods
in its ability to capture the correlations among various emotions. When
presented with the text of a tweet, the model generates confidence scores for
the presence of a wide spectrum of emotions. We later bin these confidence
scores using a $0.5$ threshold to binarize the output. The emotions it can
identify include anticipation, joy, love, trust, optimism, anger, disgust,
fear, sadness, pessimism, and surprise. In this study, we focus on the four
basic emotions of anger, fear, sadness and joy. We compare the performance of
the classifier against a manually coded sample of $1K$ tweets (See Table 12).
The performance is shown in Table 10. Barring sadness, the model’s performance
is fairly robust as noticed by the high F1 scores. We show the daily share of
all tweets expressing anger, fear, sadness and joy for liberals and
conservatives in Fig. 10(a)-(d).

(a) Anger

(b) Fear

(c) Sadness

(d) Joy

(e) Hate speech

(f) Toxicity

(g) Obscenity

(h) Insult

Figure 10: (a)-(d). Highlights the daily fraction of all tweets expressing
anger, fear, sadness and joy respectively, for liberals and conservatives. (e)
hate speech, (f)-(j) shows toxicity, obscenity, insult.

####  4.3.3 Hate Speech

Dictionary based detection of hateful content Siegel et al. [2021] often
struggles to factor in context. In this study, we use the hate speech
detection model 999https://huggingface.co/cardiffnlp/twitter-roberta-base-hate
described in Barbieri et al. [2020]. This model was trained on a Twitter data
retrained RoBERTa Liu et al. [2019] model, further finetuned on the
SemEval-2019 Task 5’s HatEval dataset as a downstream task. The HatEval
dataset discussed in Basile et al. [2019] predominantly comprises of a
multilingual dataset of hate speech tweets against women and immigrants. Given
that our study primarily revolves around the abortion rights debate in the
U.S., a model finetuned to detect hate speech against women is ideal. The
classifier provides a continuous value from 0 to 1 as its output which
quantifies the model’s confidence of a piece of text expressing hate. We then
binarize this using a $0.5$ threshold to classify the tweet as hate ($<0.5$)
and not-hate ($\geq 0.5$).

####  4.3.4 Toxicity

To measure toxicity in tweets, we used Detoxify Hanu and Unitary team [2020],
an open-source classifier. Detoxify is trained on multi-label toxic comment
classification task to predict the level of toxicity in a tweet. The model
outputs a score (a continuous value between 0 to 1) that captures the
likelihood the tweet expresses toxicity, severe toxicity, obscenity, a threat,
an insult, or an identity attack. Figure 10(f)-(j) shows the share of daily
tweets expressing toxicity, obscenity, insults, severe toxicity and threat for
liberals and conservatives. Our dataset contains more tweets expressing toxic,
obscene and insulting tweets than threats or severe toxicity. Less than 1% of
the tweets in our dataset expresses severe toxicity and less than 2% involve
threats. As such, these two aspects are not analyzed in this work. Toxicity of
both liberal and conservative users increases over time, suggesting rising
partisan animosity. Liberal users also use more insulting and obscene language
in their tweets than conservative users. All types of toxic language increase
suddenly on the day of the Dobbs ruling.

####  4.3.5 Sentiment

Sentiment classification is more simplified in comparison to emotion
detection. Instead of the more nuanced categorization of an individual’s
feeling at an instant, sentiment classification aims to identify whether the
feeling is positive, negative or neutral. Lexical approaches Warriner et al.
[2013], Hutto and Gilbert [2014], Dodds et al. [2015] have been extensively
leveraged by prior studies to identify sentiment expressed in a body of text.
The lack of contextual cues and use of free flowing text and slang influenced
the use of large language models to classify sentiment. Sentiment provides a
robustness check on inferences drawn from emotions.

We used a roBERTa based sentiment model fine-tuned on 58M tweets described in
Barbieri et al. [2022] to classify the sentiment of tweets as positive,
neutral or negative. We validated the classifier results against the manually
coded tweets showing good performance (see Table 11).

Table 11: Sentiment Performance Sentiment | Accuracy | Precision | Recall | F1
---|---|---|---|---
Positive | 0.959 | 0.743 | 0.714 | 0.728
Negative | 0.858 | 0.825 | 0.952 | 0.884

(a) Fraction of Positive sentiment on total tweets

(b) Fraction of Positive sentiment on original tweets

(c) Fraction of Negative sentiment on total tweets

(d) Fraction of Negative sentiment on original tweets

(e) Fraction of Neutral sentiment on total tweets

(f) Fraction of Neutral sentiment on original tweets

Figure 11: Fraction of sentiment by user ideology.

We found that overall, 61% of tweets have a negative sentiment, 30% are
neutral, and 9% of tweets have positive sentiment. This stands in stark
contrast to the strong positivity bias of human language, including much of
online speech Dodds et al. [2015]. Figure 11 shows the fraction of daily
tweets with positive, negative, and neutral sentiment over time. We observe
that a larger portion of the tweets by conservatives are positive than for
liberals (also see Table 8) in line with what we saw in Fig. 1(e).

Conservative users post marginally more negative original tweets as a share of
total tweets compared to liberal users, although the majority of tweets by
both liberal and conservative users have negative sentiment. Similar
observations were made in Fig. 1(b). We also observe an increase in the
fraction of negative tweets after the leak, especially among conservatives, in
agreement with the rise of anger in that group. Decrease in positive sentiment
by both sides following the leak is similar to the suppression of joy.

####  4.3.6 Validation Codebook

We validated the emotions, and sentiment classifiers using manual coding. The
codebook for human annotators is specified below. Human annotators categorized
the emotions, hateful content, and sentiment of each tweet. Following are the
concepts, categories, and examples of each variable. After 2 training
sessions, 2 graduate coders coded independently through the same 100 tweets to
establish inter-coder reliability for - anger
($Cronbach^{\prime}s\;\alpha=0.89$), fear($Cronbach^{\prime}s\;\alpha=0.96$),
sadness ($Cronbach^{\prime}s\;\alpha=0.65$), joy
($Cronbach^{\prime}s\;\alpha=0.87$), and sentiment
($Cronbach^{\prime}s\;\alpha=0.87$). The two coders then coded 450 tweets each
to have a manual coding sample to validate the machine coding result. We show
the validation results of the automatic classifiers in the following sessions.

Table 12: Codebook followed to generate manual annotations. Affect |  Type |  Label |  Example
---|---|---|---
Emotions |  Anger |  0 = No, 1 = Yes. |  (yes) @KenPaxtonTX Stop wasting our money. Wtf is wrong with you. How the hell do ppl keep voting for you. WE CANT AFFORD GAS AND YOURE WORRIED ABOUT THIS SHIT.
Fear |  0 = No, 1 = Yes. |  (yes) An abortion ban will result in more men committing murder. This isn’t hysteria or a false panic. It’s an objective truth. Homicide is the number 1 cause of death for pregnant women. Boyfriends and husbands who don’t want to have a baby will take matters into their own hands.
Sadness |  0 = No, 1 = Yes. |  (yes) @RexChapman My mum had a stillborn before I was born in 1969, and she had to carry it to term, from about 7-8 months pregnant. It nearly killed her.
Joy |  0 = No, 1 = Yes. |  (yes) @GovWhitmer Thank you for fighting for us.
Sentiment |  |  0 = negative |  @shaylarmyers @RickCarusoLA thinks LA voters are stupid. He’s lying through his teeth cuz he knows no anti-abortion candidate could every win in LA. He lost the primary he will lose the general.
|  1 = positive |  @GovWhitmer Thank you for fighting for us
Hateful speech |  |  0 = no |  "These crimes against the people will never be completely ended ‚Äî and the guarantee of reproductive care and justice will never be fully realized until capitalism is defeated." @PopResistance nhttps://t.co/qLBPTDgG9L
|  1 = yes |  Calling Evangelicals for Biden…anyone home? We are truly seeing the madness, the incredible evil, of the secular left. They LOVE death, they LOVE abortion, and they will take YOUR tax dollars to make sure babies can die *everywhere.* https://t.co/AFBUmjLFTC

###  4.4 Interrupted Time Series Analysis

We use interrupted time series design to quantify the fluctuations in online
expressions, test their significance and disentangle the heterogeneous effects
of user ideology. Interrupted time series analysis Wagner et al. [2002]
identifies discontinuities in trends in response to a treatment, in our case
an event. The four events we focus on are: the leak of SCOTUS’s draft ruling
(May 3rd, 2022), the official Dobbs ruling (June 24th, 2022), the Kansas
referendum (August 3rd, 2022) and the US midterm elections (November 8, 2022),
all of which generated online discussions about abortion rights. We examine
changes in trends at a 4-hour resolution over a time period covering the week
preceding and following the event (see Fig. 13). Our regression models are
designed to quantify the discontinuity in affect within each ideological group
immediately after the event. We define separate regression models for liberals
and conservatives. A more positive coefficient for the regression coefficient
indicates that the group (liberal or conservative) had an increase in the
outcome.

We define two separate regression models, one for each ideological group, to
identify discontinuities in trends in response to a the events. The models are
specified as:

$Y_{w}=\beta_{0}+\beta_{1}time_{w}+\beta_{2}treatment_{w}+\beta_{3}time\,post\,treatment_{w}+\epsilon$

where, $Y_{w}$ the outcome variable, is the share of tweets that in a 4-hour
window $w$ that express a particular affect. $time$ is a continuous variable
indicating the time passed in number of 4-hour windows since the occurrence of
the event (treatment), treatment is a dummy variable encoding whether the time
window $w$ was before or after the event (before = 0, after = 1) and, days
post treatment is a continuous variable with 0 before the treatment and the
number of 4-hour windows passed since the treatment. $\beta_{1}$ is the slope
of the trend in affect before the event, $\beta_{3}$ is the change in slope
post-event, and $\beta_{2}$ is the “jump” in affect immediately post the
event’s occurrence. We model this separately for liberals and conservatives
for each affect of interest and using $\beta_{2}$, we test the hypothesis that
affect increased/decreased for a particular group immediately post-event.

In Fig. 13, we present regression plots illustrating the trends for liberals
and conservatives before and after the event. The light-blue and light-red
scatter points denote the pre-event trends for liberals and conservatives,
while the dark-blue and dark-red scatter points depict their respective post-
event trends. We see significant shifts in the regressions post occurrence for
most events as discussed in the main paper.

Ruling Leak |  |  |  |  |  |
---|---|---|---|---|---|---
Official Ruling |  |  |  |  |  |
Kansas Referendum |  |  |  |  |  |
Midterm Elections |  |  |  |  |  |
Table 13: Interrupted Time Series Regression Plots. Trends for liberals and
conservatives before and after the event. The light-blue and light-red scatter
points denote the pre-event trends for liberals and conservatives, while the
dark-blue and dark-red scatter points depict their respective post-event
trends.

###  4.5 Frames

To identify and measure the prevalence of key aspects of the abortion rights
debate in the U.S., we first curated phrases and keywords that can identify
discourse related to religion, bodily autonomy, fetal personhood, women’s
rights and fetal viability in the context of abortion. The curation of these
phrases and keywords is done by leveraging, SAGE, a keyword extraction method
discussed in Eisenstein et al. [2011]. SAGE identifies keywords by assigning
each word in a document a score that represents its prominence compared to a
baseline document. More specifically, SAGE calculates the deviation in log-
frequencies of words from a baseline lexical distribution.

(a) Frames: Activity Likelihood

(b) Religion

(c) Bodily Autonomy

(d) Fetal Personhood

(e) Women’s Health

(f) Exceptions

Figure 12: (a) The log-odds plot highlights the level of engagement across
each frame for liberals and conservatives. While, liberals are more likely to
talk about women’s health and bodily autonomy, conservatives prefer engaging
about fetal personhood and religion. Geoplots in (b)-(f) show the
Z-transformed share of tweets relevant to religion, bodily autonomy, fetal
personhood, women’s health, and fetal viability for each state.

We applied SAGE on frame-relevant Wikipedia articles (See Table 14) while
using a random concatenation of articles across all five frames as a baseline
document. We use SAGE to identify frame-relevant n-grams, when $n\in[1,3]\\}$.
We manually verified the keywords identified by SAGE to ensure precision and
relevance. Table 15 lists all the keywords/phrases extracted for each frame.
While advances have been made in topic-modeling approaches Blei et al. [2003],
Peinelt et al. [2020], Grootendorst [2022], the stochasticity in terms of
topics detected dictated our choice towards a statistical approach in
Eisenstein et al. [2011] that allowed us to define the frames of interest.

Upon identifying relevant phrases, we identify tweets that contain them. The
statistics of tweets identified using this approach are shown in Table 16. The
goal is to automatically identify all words used in association with these
anchors, i.e., words that were used significantly more frequently to modify
frame-relevant terms than general terms. More specifically, these modifying
terms are adjectives, verbs, and nouns that are associated with the anchor
term. We identify them by applying part-of-speech tagging
101010https://spacy.io/usage/linguistic-features#pos-tagging and dependency
parsing 111111https://spacy.io/api/dependencyparser on all tweets identified
to be frame-relevant.

In the next step, our goal is to automatically identify all words used in
association with the anchor terms described in Table 15. These identified
words are ones that likely to be used by liberals and conservatives to modify
their abortion related discourse. To identify modifying words, we applied
part-of-speech and dependency parsing to all frame-relevant tweets (See Table
16. The dependency parsing adopted rules discussed in Card et al. [2022] and
is shown in Table 17. For a given anchor term and frame, we collected all
adjectives, verbs, and nouns that follows these dependency relations to the
anchor term. For example, we collected the adjective modifier “harmful”
directed towards the anchor term “prolife” in the phrase “harmful prolife”.
Using this approach, we collected five different corpora, one for each frame
that consists of all the adjectives, nouns and verbs that modify the issue-
relevant anchors.

Table 14: Wikipedia Articles relevant to the five frames of interest - Religion, Fetal Rights, Bodily Autonomy, Fetal Viability and Exceptions and Women’s Health. Frame |  Wikipedia Articles
---|---
Religion |  Religion and Abortion, History of Christian Thought on Abortion, Christianity and Abortion, Catholic Church and Abortion, Abortion and the Catholic Church in the US, Ensoulment
Fetal Rights |  Fetal Rights, Born Alive Laws in the US, Unborn Victims of Violence Act, Heartbeat Bill, Prenatal Perception, Beginning of Human Personhood, Philosophical Aspects of the Abortion Debate
Bodily Autonomy |  My Body My Choice, United States Abortion Rights Movement, Medical Abortion, Planned Parenthood, Women’s Health Protection Act, Bodily Integrity, Reproductive Justice
Fetal Viability and Exceptions |  Pregnancy from Rape, Minors and Abortion, Late Termination of Pregnancy, Fetal Viability, Ectopic Pregnancies, Pregnancy Complications
Women’s Health |  Women’s Health, Mifepristone, Abortifacient, Hysterotomy Abortion, Dilation and Curettage, Unsafe Abortion, Self-Induced Abortion, Sexual and Reproductive Health, Maternal Mortality in the US, Birth Control in the US, Emergency Contraception, Abortion and Mental Health
Table 15: Anchor terms extracted from Wikipedia articles using SAGE Eisenstein et al. [2011] for each of the five frames - Religion, Bodily Autonomy, Fetal Personhood, Women’s Health and Exceptions. Frame |  Phrases
---|---
Religion |  god, abortion is morally, american catholics, anglican, aquinas, archbishop, augustine, baptist, bishop, catholic, christ, christian, christianity, church, communion, ensoulment, evangelical, excommunication, good catholic, holy, human soul, lutheran, methodist, morally wrong, nrlc, penance, pius, pope, presbyterian, protestant, sacrament, sexual immorality, sinful, soul, synod, prayer
Bodily Autonomy |  abortion access, abortionaccess, abortion is a humanright, abortion is essential, abortion is healthcare, abortion rights, abortion rights are human rights, access to abortion, american taliban, reproductive autonomy, bans off our bodies, bodily autonomy, bodily integrity, bodily right, coat hanger, codify roe, coerced sterilization, forced birth, forced sterilization, forcedbirth, freedom of choice, herbal, her body her choice, induce abortion, knitting, mybody, my body my rights, my choice, parenthood clinic, planned parenthood, pro choice, pro-choice, prochoice, promaternalhealth, provide abortion, rcrc, reproductive choice, reproductive freedom, reproductive justice, reproductive right, reproductive freedom, reproductive justice, reproductive right, reprorights, right to choose, roevember, roevemberiscoming, safe abortion, saveroe, scienceoverideology, scotusiscompromised, scotusiscorrupt, self induced aboriton, unsafe abortion, waronwomen, wewontgoback, womens right, womenshealth, womensright, womensrightsarehumanrights
Fetal Personhood |  abortion Is murder, abortion Is not a right, abortion is racist, abortion is violence, abortion kills babies, antilife, babies lives matter, choose life, end abortion, life is a human right, life matters, life wins, live action ambassador, love them both, march for life, pregnancy center, pro-life, probirth, pro choice violence, pro control, pro family, prolife, prolife gen, prolife generation, rescue the preborn, right to life, save the babies, save the baby humans, value life, violent protestor, we will end abortion, abortion kills babies, adoption, babies lives matter, baby right, baby rights, cerebral cortex, don marquis, end abortion, fetal pain, fetal right, fetalright, fetal right to life, heartbeat, hominization, infanticide, love them both, marquis argument, personhood, rescue the preborn, right to life, save the babies, save the baby humans, unborn
Women’s Health |  abortifacient, abortion is a human right, abortion is healthcare, abortion rights are humanrights, adverse effect, antiglucocorticoid, antiprogestogen, birth control, coat hanger, comstock bill, contraception, contraceptive, curettage, curette, ethinylestradiol, female sterilization, gemeprost, hysterotomy, increase maternal, intercourse, intrauterine, iuds, levonorgestrel, maternal death, maternal health, maternal morbidity, maternal mortality, medication abortion, mental health, methotrexate, mifegyne, mifepristone, misoprostol, mortality ratio, oral contraceptive, postcoital, potassium permanganate, prenatal care, progesterone, progesterone receptor, progestin, progestogen, promaternalhealth, prostaglandin, reproductive, reproductive hazard, reproductive health, reproductivefreedom, side effect, spermicide, sterilization, tetragynon, uclaf, ulipristal, ulipristal acetate, unintended pregnancy, vasectomy, mothers health
Exceptions |  abnormalities, abnormality, adoption, chance of survival, consent notification, ectopic, exception, gestation, incest, late term, limit viability, malformation, minor abortion, minor preganancy, parent consent, parental consent, parental involvement, parental notification, premature, preterm, rape, rapist, require second physician, survival, underage, viability, viable, week limit, weeks
Table 16: Number of tweets and original tweets relevant to each frame. Frame | #Tweets | #Original Tweets
---|---|---
Religion | 2,709,287 | 182,480
Bodily Autonomy | 7,257,934 | 492,488
Fetal Personhood | 2,730,391 | 200,396
Women’s Health | 5,564,412 | 301,068
Exceptions | 5,442,829 | 171,643
Table 17: Dependency rules used to identify adjectives, verbs and nouns associated with the anchor terms. Part-of-Speech | Dependency Path to Anchor
---|---
Adjective |  XX-amod$\rightarrow$ANC
|  XX-amod $\rightarrow$ YY $\leftarrow$ amod-ANC
| ANC-nsubj$\rightarrow$XX
Verb (Subject) |  XX-relcl$\rightarrow$ANC
|  XX-acl$\rightarrow$ANC
Verb (Object) | ANC-dobj$\rightarrow$XX
| ANC-pobj$\rightarrow$YY-prep$\rightarrow$XX
Noun | ANC-amod$\rightarrow$XX
| ANC-compund$\rightarrow$XX