Partisanship and views of the court

At its core, partisan dynamics significantly shape public perceptions of the Court and its functions. While non-political factors, such as support for the rule of law, do influence reactions to Court rulings, research consistently shows that partisanship and ideology hold enduring sway (Bartels and Johnston Reference Bartels and Johnston2013, Reference Bartels and Johnston2020; Christenson and Glick Reference Christenson and Glick2015; Nicholson and Hansford Reference Nicholson and Hansford2014). Bartels and Johnston (Reference Bartels and Johnston2020) argue that expecting the public to support the Court regardless of their political leanings is unrealistic; they propose that public backing for the Court is primarily driven by policy considerations rather than procedural ones. Individuals perceive rulings that align with their beliefs as fair and just, while those contrary to their views are often viewed as politically motivated and unfair. The substantial influence of partisanship, predominantly explored within the context of the Court’s decisions, may likewise permeate the process of selecting justices.

Scholars have long voiced concerns about politicizing the appointment process and its potential to undermine public confidence in the Supreme Court. Carter (Reference Carter1993) highlighted this issue over three decades ago, cautioning that the tendency of opposition to unearth negative information to discredit nominees could weaken the Court as an institution. He warned, “Our public institutions are at risk when the public has grave doubts about the nominee, and wishfulness is no substitute for the cold calculation that sometimes requires our politicians to realize that getting their way will cause more harm than good” (73). Indeed, contemporary survey-experimental scholarship has borne out Carter’s predictions, demonstrating that partisanship affects how the public views (hypothetical) nominees (Chen and Bryan Reference Chen and Bryan2018; Hoekstra and LaRowe Reference Hoekstra and LaRowe2013; Sen Reference Sen2017) as well as the Court, itself, in the wake of politicized nominations. In an experimental design, for example, Rogowski and Stone (Reference Rogowski and Stone2021) vary respondents’ exposure to different kinds of elite messaging modeled after real examples. They find that messaging that criticizes a nominee activates political identities, resulting in strong preferences for in-party nominees.

While these findings may suggest our inquiry is redundant, the impact of ideology and partisanship on support for real nominees is not nearly so uniform as the results found in survey experiment literature. For example, research by Badas and Stauffer (Reference Badas and Stauffer2018) contains survey data from seven nominees (Alito, Breyer, Garland, Kagan, Roberts, Sotomayor, and Thomas), where respondent ideology is a significant predictor of support for all but two (Breyer and Alito), however partisanship is much less impactful. Specifically, Badas and Stauffer (Reference Badas and Stauffer2018) find respondent partisanship is a significant predictor of support for Kagan and Sotomayor, however insignificant for Thomas. Their models for Breyer, Roberts, Alito, and Garland are reported using dummy variables for Republican and Democrat (rather than the party ID scale used for the others), and only three of eight of these dummy variables are significant predictors of support for the nominee: Republicans were more likely to support Roberts and Democrats more likely to support Breyer and Garland. We note, however, that the most recent survey in Badas and Stauffer (Reference Badas and Stauffer2018)’s work – Garland – is often cited as an inflection point in politicization of nominations (Zilis and Blandau Reference Zilis and Blandau2021; Truscott Reference Truscott2023).

Looking to work that examines how support for the Court is affected by these more recent nominations and vacancies, scholars often point to the now-politicized nomination and confirmation process as a source of partisan and ideological effects. Krewson and Schroedel (Reference Krewson and Schroedel2020) measure diffuse support for the Court in the aftermath of Kavanaugh’s confirmation, concluding, “It appears that the partisan hearings may have caused partisans to view the Court differently” (1435). Similar work examining Barrett’s confirmation proceedings argues, “It would be surprising if politicized and partisan confirmation hearings do not impact people’s perceptions of judiciaries and judging,” and indeed this study goes on to identify a drop in institutional support for the Court among Democrats (Krewson Reference Krewson2023).

These same patterns – where partisanship impacts support for the institution of the Court – have even been observed immediately after vacancies, i.e., before there’s a particular nominee to criticize or ‘partisan hearings’ to observe. Again, scholars similarly attribute these effects to the politicized nature of these more recent vacancies and hearings. Glick (Reference Glick2023) found that Democrats’ diffuse support for the Court dropped immediately after Justice Ginsburg’s death, as the “intense politics around the unexpectedly open seat” became clear President Trump would appoint her successor (104). Similarly, Armaly (Reference Armaly2018) found increased diffuse support among Democrats immediately after Justice Scalia’s death, but before Senate Majority Leader Mitch McConnell announced that Republicans would not vote on any Obama nominee. In other words, Democrats’ support for the Court rose when they believed President Obama would appoint Scalia’s replacement.

In general, recent work that examines how support for the Court is impacted by politicized nominations (and vacancies) paints a clear picture for the role of partisanship and ideology as driving support or disapproval for the institution of the Court, where party or ideological alignment increases support. While such findings have not always been present, and indeed were the opposite in the aftermath of Justice Alito’s confirmation (Gibson and Caldeira Reference Gibson and Caldeira2009), research from the last ten years uniformly suggests that the public now views the Court through a partisan screen of ordinary politics. Scholarship looking at indicators of support for nominees, however, is primarily focused on nominations from 10 to 30 years ago and does not identify such a uniform role for partisanship and ideology. Our study aims to bridge some of the gaps in the existing literature by providing real-time insights into online discourse and opinion formation during confirmation processes. Twitter, as a platform for immediate and widespread communication, offers a unique window into the Twitter public’s reactions to nominees and the confirmation process. By analyzing tweets related to confirmation proceedings, we can discern trends in sentiment, identify influential topics, and explore how partisan dynamics manifest in online discussions. This approach complements traditional survey-based research by capturing spontaneous and unfiltered reactions from a diverse range of voices, thereby providing a more comprehensive understanding of how the public engages with and perceives Supreme Court nominations in today’s digital age. Now, we turn to a discussion about these digital voices and why we should study them.

What is the value of social media data?

In today’s interconnected world, the value of social media data is derived from the way people utilize a platform, and this is particularly apparent with sites like Twitter. Beyond the confines of dinner tables and office corridors, Twitter serves as a bustling public square, where ideas clash, alliances form, and narratives evolve in real-time. This digital sphere functions as a sort of living laboratory where users create expressive content and thus enable researchers to explore user opinion in reaction to current and political events. Unlike the static snapshots provided by traditional polling and survey methods where attitudes are collected using likert scales and feeling thermometers, Twitter data offer richer texts, capturing the ebb and flow of online discourse in its raw, unfiltered state.

In exploring the significance of Twitter as a medium for political opinions, it’s vital to acknowledge the platform’s vast reach and popularity, boasting approximately 330 million monthly active users and 192 million daily active users (Twitter 2021). Despite this widespread usage, we must also recognize that Twitter users are not a perfectly representative sample of the general public. The platform’s user demographics, skewed towards urban areas, individuals under the age of 50, and predominantly male users, introduce inherent biases (Wojcik and Hughes Reference Wojcik and Hughes2019; Mislove et al. Reference Mislove, Lehmann, Ahn, Onnela and Rosenquist2011). While Twitter’s user base is evolving with increased gender and racial diversity over time, it’s essential to approach the data with an awareness of its limitations. Moreover, our study’s focus on analyzing tweets, not users, means we observe behavior only from those who have chosen to express their opinions about Supreme Court confirmations. We address these concerns in four ways, detailed below.

First, because we select upon engagement with the Court, we pay careful attention to the effect of political engagement in the wider Courts literature, specifically whether political engagement aligns with partisan effects such that our sample pre-determines results. Many Courts-based surveys and survey experiments utilize political knowledge; however, knowledge seems a poor proxy for tweeting. To overcome this, we identify a handful of studies that utilize self-reported measures of engagement. Most broadly, higher levels of political engagement are associated with increased support for the Court as an institution (Bartels and Johnston Reference Bartels and Johnston2020; Gibson and Caldeira Reference Gibson and Caldeira2009),Footnote ³ while respondents’ engagement has no impact on support for nomination-specific Court-curbing items (Bartels and Johnston Reference Bartels and Johnston2020, 123). These findings align with survey data reported by Badas and Stauffer (Reference Badas and Stauffer2018) showing respondents who more closely followed Clarence Thomas’s confirmation were more supportive of his confirmation. These studies suggest that having a Twitter sample with high levels of political engagement/interest in confirmation proceedings shouldn’t skew our results in the direction of predicted partisan effects; indeed, Bartels and Johnston (Reference Bartels and Johnston2020) found that engagement had a (positive) “potent effect” on support for the Court, even in relation to the negative effects of policy disagreement (116).

Second, we take steps to demonstrate that our Twitter data align with prior work which finds that sentiment in tweets tracks with public approval data. To do so, we replicate O’Connor et al. (Reference O’Connor, Balasubramanyan, Routledge and Smith2010)’s study – a seminal work in the field that’s been cited over 2,500 times. Specifically, O’Connor et al. (Reference O’Connor, Balasubramanyan, Routledge and Smith2010) collected tweets about President Obama and showed that aggregate sentiment of those tweets did, indeed, track with presidential approval polling. To replicate this work, we replace presidential approval polling and data harvested from tweets about the President with nominee approval polling and data culled from tweets about the nominees. We report the full results of that replication in our appendix but note here that our findings are highly consistent with O’Connor et al. (Reference O’Connor, Balasubramanyan, Routledge and Smith2010)’s: we find sentiment and public approval are correlated at r = 71.1% (O’Connor et al. [Reference O’Connor, Balasubramanyan, Routledge and Smith2010] recovered r = 73.1%); the correlation statistic raises to r = 81.9% when restricting our analysis to hand-identified partisan users. This replication gives us confidence that 1) utilizing methods previously used to study the political branches is applicable to our work, and 2) despite the changing nature of political messaging and social media use, those changes have not fundamentally altered the relationship between Twitter data and public opinion polling data.

Third, we echo sentiments expressed in recent Courts scholarship which notes, “…studying the population on Twitter does allow for research designs that maintain strong internal validity, allowing us to consider the comparative statistics beyond the overall level of estimated effects” (Adams-Cohen Reference Adams and Joseph2020, Reference Adams and Joseph615). Even if one remains skeptical that our data bear any resemblance to broader public attitudes, despite the O’Connor et al. (Reference O’Connor, Balasubramanyan, Routledge and Smith2010) replication detailed above, our research still illuminates two comparative differences: reactions across the different stages of the nomination and confirmation process as well as comparative differences between hand-identified Republican and Democrat users.

Fourth, and even given the limitations noted above, Twitter data are extensively used in various disciplines and contexts to gauge public responses to products, services, and current events. For our specific objectives, studies show that Twitter users are responsive to political happenings (Wang et al. Reference Wang, Can, Kazemzadeh, Fran¸ and Narayanan2012) and engage with and share information that’s favorable to their own political party (Shin and Thorson Reference Shin and Thorson2017). Additional work finds that quantity and content of political tweets can predict public opinion (Davis et al. Reference Davis, Zheng, Liu and Levy2017; Tumasjan et al. Reference Tumasjan, Sprenger, Sandner and Welpe2010; O’Connor et al. Reference O’Connor, Balasubramanyan, Routledge and Smith2010) and offer valuable information for decision-making, candidate popularity, forecasting, and governance and public trust (Tumasjan et al. Reference Tumasjan, Sprenger, Sandner and Welpe2010; Yaqub et al. Reference Yaqub, Chun, Atluri and Vaidya2017). In many aspects, the statements made on Twitter align with the conceptual framework of Zaller’s theory of public opinion formation (Zaller Reference Zaller1992). Tweets serve as expressions reflecting individuals’ thoughts, particularly influenced or primed by current events and their interactions within the Twitter network. Similar to the simultaneous interaction of opinion, media choices, and ideology (Prior Reference Prior2007), Twitter’s environment allows individuals’ tweets to serve as partial statements about their policy opinions (Clark et al. Reference Clark, Wang and Agichtein2018).

Building upon the broader research on politics and social media, a few studies explore how Twitter users respond to events at the High Court. Darwish (Reference Darwish2019) analyzes 128,000 users to find that those who communicated support or opposition to Brett Kavanaugh’s confirmation used divergent partisan hashtags and followed different Twitter accounts. Republicans used hashtags like #walkaway (from liberalism), while Democrats utilized, for example, the partisan hashtag #TheResistance (a movement that protested the presidency of Donald Trump). Additionally, Sandhu et al. (Reference Sandhu, Vinson, Mago and Giabbanelli2019) examine two waves of tweets – collected during and then one month after Kavaunaugh’s confirmation – to look for associations between the terms “Supreme Court” and “partisanship.” The authors conclude that public opinion changed after the Kavanaugh hearings because the terms only become associated with one another in their second wave of tweets. Finally, a pair of studies use Twitter data to examine reactions to the Court’s same-sex marriage jurisprudence. Clark et al. (Reference Clark, Wang and Agichtein2018) find that two early Supreme Court decisions regarding same-sex marriage (Hollingsworth v. Perry and U.S v. Windsor) affected public discourse on the topic, polarizing both discussions and mass opinion. Recent work by Adams-Cohen (Reference Adams and Joseph2020) treats Twitter data as a proxy for survey-based public opinion data in order to test between two competing theories of public response to Court decisions that were developed and initially tested using survey data several years prior to the advent of social media (structural response theory and backlash theory). Adams-Cohen (Reference Adams and Joseph2020) further notes the appeal of Twitter when attempting to gather contemporaneous data for unscheduled political events like Court opinions and vacancies.

In essence, the value of social media data, epitomized by platforms like Twitter, lies in its ability to serve as a dynamic window into a nearly endless range of real-time reactions to political events. Taken together, the research summarized above demonstrates that Twitter users tweet about Court events, and that Twitter data bear some relationship to public opinion while not necessarily being public opinion. Indeed, every political event finds expression in millions of tweets, revealing a multitude of opinions, ideas, topics, and sentiments that reflect a broader socio-political landscape. To engage this rich source of data, our study ventures beyond the surface-level analysis of Twitter trends – hashtags, likes, and retweets – to examine non-elite partisan differences based in the content of hand-identified partisan users. By scrutinizing sentiment and dissecting the topics that dominate tweets surrounding the most recent Supreme Court nominees, we aim to uncover the subtle nuances of online political discourse in the digital age.

Hypotheses

The nomination and confirmation of Supreme Court justices are highly consequential events that have far-reaching implications for the ideological balance and direction of the Court. From the moment a vacancy on the Court arises, political stakeholders, including party leaders, interest groups, and engaged citizens, immediately focus their attention on the potential nominees and their implications for the Court’s future decisions (Armaly Reference Armaly2018; Glick Reference Glick2023). While Gibson and Caldeira (Reference Gibson and Caldeira2009)’s work demonstrated that the public was largely indifferent to partisan messages in a politicized Alito confirmation process, many survey-experiment works since then have found that ideology and partisanship shape individuals’ perceptions and attitudes towards hypothetical judicial nominees (Hoekstra and LaRowe Reference Hoekstra and LaRowe2013; Chen and Bryan Reference Chen and Bryan2018; Sen Reference Sen2017), and especially when political rhetoric about a nominee is invoked (Rogowski and Stone Reference Rogowski and Stone2021). Additional recent work examining impacts on support for the Court in the wake of the Kavanaugh and Barrett proceedings reveal that partisanship affects views of the Court, and these findings were also reflected in work that captured survey data surrounding the vacancies created by the deaths of Justices Ginsburg (Glick Reference Glick2023) and Scalia (Armaly Reference Armaly2018).

These findings suggest that Democrats and Republicans are inclined to approach vacancies and the confirmation process with preexisting ideological and partisan preferences, influencing their reactions to nominees aligned with or divergent from their party platforms. Partisans, driven by their political preferences and agendas, are likely to seize this opportunity to vocalize their support or opposition to specific nominees, advocate for nominees aligned with their party’s ideology, and criticize those they perceive as threats to their policy objectives. Building on the insights of Bartels and Johnston (Reference Bartels and Johnston2020), perhaps confirmation processes aligned with an individual’s partisan beliefs may be perceived as fair and impartial, whereas those diverging from their views are construed as politically motivated and unjust. We believe this overtly political messaging will extend even to the vacancy stage, i.e., before there’s a nominee to praise or criticize.

In the context of Twitter, we anticipate these preferences to manifest in the sentiment of tweets and the topics discussed. Throughout each stage of the confirmation process, we expect partisan Twitter users to express more favorable sentiments when aligned with a nominating president. Additionally, we expect that partisan differences will emerge in topics discussed, with presumptive losers attacking the process with partisan language.

Data and methods

We begin data collection by capturing tweets via a programmable spreadsheet tool called TAGS that links to the Twitter Search API. The Twitter Search API is used to retrieve past tweets matching a specific criterion (i.e., keyword or hashtag) within a designated search window. However, for each request we can only retrieve a 10% random sample, and Twitter does not provide any description of the algorithms used to generate the random samples.

As we are interested in sentiment and topics during Brett Kavanaugh, Amy Coney Barrett, and Ketanji Brown Jackson’s confirmation processes, we used the API tool to search for several terms at four distinct time periods: vacancy, nomination, confirmation hearings, and confirmation. We gather tweets during each of these stages and continue for roughly 24 hours after each stage. Search terms such as #SCOTUS and “confirmation hearing” are used, along with the names of the nominees and the justices who departed the bench. The included terms are non-partisan and specified using non-case-sensitive identifiers (meaning the API picks up “scotus” in addition to “SCOTUS”) in order to create a broad and facially non-partisan dataset. A full list of the stages and search terms used is available in Table A1 of the online supplemental materials.

Raw data in hand, amounting to more than 1.6 million tweets for Kavanaugh, 2.4 million for Barrett, and 3.5 million for Jackson, we first followed the lead of past scholars who have studied participation on Twitter and excluded retweets from our analysis (Hemphill, Otterbacher, and Shapiro Reference Hemphill, Otterbacher and Shapiro2013; Mazoyer et al. Reference Mazoyer, Julia Cag´e and Hudelot2020). This left us with roughly 603,000 initial tweets across the three confirmation processes.Footnote ⁴ In essence, this initial number of tweets can be broken down by nominee, their stage in the nomination and confirmation process, and finally by partisanship (described next). Table A2 of our online appendix contains the number of tweets which fall into each “cell” to give a sense of the amount of data we used during different types of analyses.

After eliminating retweets, we next identify the partisanship of a small proportion of users to help inform our hypotheses. Rather than employ machine learning (which does not filter for bots or news accounts), we use an observational approach to identify the partisanship of Twitter users. We employ a cadre of research assistants to examine each user’s page, read recent tweets, and ascertain from those tweets if the user appeared to be a Republican, Democrat, Independent, supporter of a third party, or was unidentifiable. We use standard inter-coder reliability measures and resolve any discrepancies in the data ourselves. We make two efforts to avoid any endogeneity problems; first we carefully exclude from sentiment analysis any tweets that were used to identify partisanship. In other words, the tweets we used to code a user as a Republican or a Democrat are exogenous to users’ tweets about the Court or the confirmation process, and often occurred months before or after the confirmations took place. Because of the time-intensive nature of coding partisanship paired with the sheer number of users we have, we randomly select approximately 4% of users who tweeted at any point during each of the three confirmations, and we code this random sample for partisanship, leaving us with 21,468 partisan-identified tweets. Second, we also remove from each tweet the terms used to search for it. For example, if TAGS returned a spreadsheet of 5,000 tweets based on the search terms “SCOTUS” and “Kavanaugh,” then those terms were removed from every tweet in that spreadsheet.

To explore the sentiment of tweets during the confirmation processes, we utilize the AFINN Sentiment Lexicon (Finn Reference Finn2011). This is an open-source lexicon which originally used crowd-sourcing to manually rate more than 2400 words from indicating very negative sentiment (-5) to very positive sentiment (5). What is attractive to us about this Lexicon is that it was explicitly developed for sentiment analysis of Tweets and other “microblogs.”Footnote ⁵ Once we apply the AFINN dictionary to our data, we obtain a positive, negative, and overall sentiment score for each tweet; overall sentiment is a sum of the values for each word within the tweet, ranging from -116 (“Ok. But Amy Coney Barrett f*** you f*** f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f*** you f***”),Footnote ⁶ to 26 (“Awesome, Awesome, Awesome, Awesome, Awesome, Awesome, totally #AWESOME!!!!! Congratulations soon to BE Associate Justice of the United States Supreme Court(#SCOTUS) #KetanjiBrownJackson!!!!! Much SUCCESS!!!!!”) with a mean score of ‒0.79.

Because we are also interested in the content of partisan tweets – especially how topics that are discussed by partisans differ – we next employ a topic modeling strategy developed by Lu, Henchion, and Namee (Reference Lu, Henchion and Namee2019). Topic models were initially developed to help researchers efficiently identify latent topics contained in large, text-based datasets. Lu, Henchion, and Namee (Reference Lu, Henchion and Namee2019) build off of the traditional Latent Dirichlet Allocation (LDA) model by adding a second processing stage designed to overcome topic-level comparison difficulties, thereby making it possible to identify topics that are most dominated by documents (tweets) from a single source (Republicans or Democrats).Footnote ⁷ This allows us to first fit a single LDA model for each nominee (rather than fitting separate models by party), and then next determine which topics are particularly contributed by one party or the other.Footnote ⁸ In plain language, this method of analysis enables us to investigate differences in what partisans tweet about without having to hand-classify individual tweets.

To ensure we have ample text for our LDAs, we began this endeavor by collecting additional tweets from our partisan users. We did this using the same query terms and time periods as our initial TAGS collection, however, now utilizing Twitter’s API v2 with an academic account that allows us to search back in time. Using this augmented corpus of partisan tweets, we follow the two-stage comparative process detailed above. Topic modeling was done in Python 3 using gensim’s latent Dirichlet allocation module. For more information about LDAs, the comparative method, how we trained our models, and how we gathered additional tweets, see the online appendix.

Finally, we classify each reported topic as “partisan” or “non-partisan” by looking for the explicit use of party names (to include “GOP”) or representatives of a party (to include phrases like “Obamacare” or “Bidenomics”); incorporation of any of these terms means a topic is “partisan.” These coding rules and definitions were developed to identify partisan floor speeches (Morris Reference Morris2001, Reference Morris107), and have been recently used to research partisan rhetoric in the Twitter data of U.S. Senators (Russell Reference Russell2021).Footnote ⁹

Partisan-based differences in tweet topics

Recall our second hypothesis predicts that presumptive losers will employ partisan topics more centrally than presumptive winners, even at the vacancy stage. The results of our topic modeling comparison partially support this hypothesis. Specifically, while we find that presumptive policy losers do, often, invoke partisan themes in their most unique topics, policy winners are consistently utilizing partisan themes – where, again, we identify “partisan” language by following Morris (Reference Morris2001) and Russell (Reference Russell2021) and seeking out party names and party representatives. To help highlight these trends, we’ve underlined words that meet our definition of “partisan,” while shaded cells in Table 1 depict the top topics attributed to de-facto policy losers. We begin our analysis by focusing on the top panels of Table 1, as these panels list the two most divergent topics per party per nominee, where a divergent topic is one that identifies information most unique to a single source, Democrats or Republicans, or in other words – topics that Democrats are interested in and Republicans aren’t, and vice versa.

Table 1. All Combined Stages

Note: The top two most divergent topics by partisan users and nominees for all combined stages (top panels) and for only the vacancy stage (bottom panels) of the nomination and confirmation process. Gray cells represent de-facto policy losers with partisan terms underlined.

During proceedings for both Brett Kavanaugh and Amy Coney Barrett, Democrats’ (the policy losers) most unique topics focus on issues such as allegations of sexual assault (“believe,” “sexual,” “assault”), Senator McConnell’s blocking of Merrick Garland (“seat,” “mcconnell,” “obama,” “garland”), and the future of policies concerning gun regulations (“gun”), LGBTQ+ issues (“lgbtq,” “human,” “fear”), and health care and reproductive rights (“aca,” “healthcare,” “roevwade”). Similarly, during Ketanji Brown Jackson’s proceedings, Republicans (now presumptive policy losers) highlighted issues around the race and gender of President Biden’s nominee, with special attention paid to the fact that he said he’d nominate a black woman and then did. This was cast in the frame of Jackson having been an affirmative action choice, implying that she wasn’t qualified for the job (“woman,” “nominate black,” “action,” as in affirmative action). In addition, Republicans also focused on Jackson’s responses to two highly politicized lines of questioning from Republicans on the Judiciary Committee – trans rights (“answer,” “define woman”) and sentencing related to child pornography cases (“child porn,” “record”).

Striking and unexpected in these results are the two topics – both dominated by Democrats-as-losers – that do not include any partisan terms.Footnote ¹¹ For example, when discussing Kavanaugh’s confirmation hearings, Democrats’ most unique topic focused on allegations that Kavanaugh had sexually assaulted Dr. Blasey-Ford at a high school party; despite this issue splitting neatly along partisan lines (Newport Reference Newport2018), use of explicitly partisan language wasn’t common enough – along with discussing the sexual assault hearing – to show in the top 15 words of the topic. Similarly, Democrats’ top-topic during Barrett’s proceedings focused on the future of policies that are central to partisan agendas (gun regulations and LGBTQ+ issues) but did so without a prevalence of partisan terminology. Even Democrats’ second most unique topic for Barrett’s confirmation includes only one partisan term, “obamacare,” where it appears within a broader conversation about health care and reproductive rights (“aca,” “healthcare,” “roevwade”) – another policy area with major partisan differences.

We next examine the bottom panels of Table 1 to explore whether partisan politics comes into play, even at the vacancy stage. If partisan Twitter users see the Court as an apolitical and non-partisan institution, then – in line with Rogowski and Stone (Reference Rogowski and Stone2021) – this is the stage where we should be most likely to see non-partisan topics because there isn’t yet a particular nominee to criticize or praise. Results from the vacancy stage, however, show partisan themes being employed in five of six topics, where the only apolitical topic comes during the vacancy caused by Ruth Bader Ginsburg’s passing (Barrett vacancy). Here Democrats’ most distinct topic expressed condolences, but also contained hope that a new Supreme Court wouldn’t erode the rights she fought for along with predictions that it would: “ACA…GONE 1964 Civil Rights Act…GONE Roe v Wade…GONE Equal Housing…GONE Affirmative Action…GONE Segregation…WELCOME BACK. Think it can’t happen? Don’t f*cking kid yourself with a 6-3 SCOTUS.” As happened twice before, however, Democrats did not rely upon partisan terms. Some of this tracks as simply thanking a justice for their service or hoping they rest in peace and are non-partisan by nature. The call to action in the wake of her death, however, is surprisingly non-partisan (at least by our chosen operationalization of “partisan”): “We need your voice right now! You have to be on the front lines of this fight to keep the Supreme Court seat. You also need to be behind the scenes working on this. We need all hands on deck!” Meanwhile, Republicans urged President Trump to replace Justice Ginsburg immediately (“trump,” “replace justiceginsburg”).

Similar partisan-based trends appear during the vacancies created by Justices Kennedy’s and Breyer’s retirements. For instance, the most unique topic for Democrats once Justice Kennedy announced his retirement (Kavanaugh vacancy) predicted that President Trump would nominate someone who’d overturn Roe v. Wade: “The same people saying Roe vs Wade won’t be overturned are the same people who said Trump wouldn’t win. It can be. It will be. Fight! No Vote on #SCOTUSnominee.” On the other side, Republicans focused on Democrats and the left, discussing then-Senate majority leader Harry Reid’s suspension of the filibuster for Court of Appeals nominees as well as liberals in general, with phrases like “liberal meltdown,” “liberal tears,” and “liberal weenies.”Footnote ¹² Similarly, when Justice Breyer announced his retirement (Jackson vacancy) – Republicans lamented President Biden’s campaign promise to nominate a black woman (“Biden,” “pick,” “race”), calling it identity politics and race-based profiling (“Apparently the only qualifications for being a Supreme Court justice is [sic] race and gender.”), and Democrats, now presumptive winners, invoked past partisan grievances by mainly focusing on the Republican effort to block Merrick Garland in 2016 (“McConnell,” “republican,” “seat”).

Finally, we note that – despite the prevalence of partisan terminology used – presumptive winners often discuss their nominee in less partisan (if not non-partisan) terms, placing emphasis on procedural elements of the process and the nominee’s qualifications. The bigram “confirmation hearing” a non-partisan term appears in the top two topics of the presumptive winners for each nominee, but not for any presumptive losers. In addition, supportive partisans often tweet headlines or news links containing factual information (i.e., Kavanaugh sworn in as 114th justice, hours after senate votes to confirm) or simple congratulations (Congratulations justice Kavanaugh!). Certainly, partisan themes are present for presumptive winners, evident in things like “hillary,” “trump,” “dems,” and the slate of tweets about then-Senator Kamala Harris’s questioning of Amy Coney Barrett. However, when “winners” do use partisan topics, they often focus on past or other related grievances. Take, for example, the most unique topic contributed by Democrats during Jackson’s process – this topic is dominated by discussions of Ginni Thomas’s involvement in trying to overturn the 2020 presidential election (“trump,” “clarence thomas,” “wife,” “ginni,” “election”). Similarly, the top-topic for Republicans during Kavanaugh’s proceedings includes references to Hillary Clinton, who lost to Donald Trump two years earlier: “Trump’s victory stopped Hillary from appointing 2 or 3 justices and 167 to the federal judiciary.” This finding falls in line with survey and survey-experimental work that posits policy wins and losses are strategically framed differently, where wins highlight “procedural appropriateness” and losses are due to some type of inappropriateness, whether it be political or procedural (Bartels and Johnston Reference Bartels and Johnston2020, 28).