Pith. sign in

REVIEW 3 major objections 6 minor 143 references

The Role of Partisan Culture in Mental Health Language Online

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read In 2.18 million matched posts, Democrats use clinical language and Republicans social language to describe distress.

desk verdict A promising descriptive study of partisan idioms of distress whose headline p-values likely don't survive user-level clustering; worth reviewing with a request for re-analysis. read the letter →

arxiv 2506.20377 v1 pith:7TK5VEHA submitted 2025-06-25 cs.HC cs.CYcs.SI

Sachin R. Pendse , Ben Rochford , Neha Kumar , Munmun De Choudhury This is my paper
classification cs.HCcs.CYcs.SI
keywords mentalhealthpartisancultureonlinecommunitiesidiomofdistresspoliticalpolarizationlanguageanalysisstatisticalmatchingReddit
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that American partisan culture, the shared norms and worldviews attached to being a Republican or Democrat, is not confined to politics: it also shapes how people talk about psychological distress. It analyzes more than two million posts from carefully matched users of online mental health support communities and finds consistent differences in expression. Democrat users use more clinical psychiatric language and more polarization-related words, while Republican users describe distress more through social relationships, work, money, and faith. The paper argues that recognizing these partisan idioms of distress matters for designing support platforms, for clinicians who may miss non-clinical expressions, and for building understanding across partisan lines.

What carries the argument

The load-bearing mechanism is a matched-cohort observational design. Partisan users are identified from their posting histories in partisan communities, then paired 1-to-1 on demographic-proxy and activity covariates such as number of subreddits, posting rate, and function-word usage, so that the two groups differ mainly in partisan identity rather than in who uses the forums. Language is then compared through three complementary lenses: a psycholinguistic word-count dictionary, an open-vocabulary model that finds words most distinctive to each group, and two targeted lexicons measuring clinical mental-health terms and polarization-related terms. The idiom of distress, a culturally shaped way of expressing psychological suffering, is the conceptual object the analysis is built to detect.

What would settle it

Re-run the Democrat-versus-Republican comparisons with standard errors clustered by user instead of treating every post as independent; if the differences in clinical-language incidence ($17.75\%$ vs $16.43\%$) and polarization-language incidence ($37.10\%$ vs $35.61\%$) no longer reach the 95% confidence level, the central claim of measurable partisan differences loses its quantitative support.

Watch

Extended reading notes

Core claim

The paper's central finding is that matched Republican and Democrat users of online mental health support communities express distress in measurably different ways. Democrat users employ clinical mental-health language in $17.75\%$ of posts versus $16.43\%$ for Republican users ($p<10^{-101}$), and polarization-related language in $37.10\%$ of posts versus $35.61\%$ ($p<10^{-79}$). Republican users, by contrast, use more social language, on average $12.93\%$ of a post versus $12.32\%$ for Democrats ($p<10^{-163}$), and also use more language related to work, home, money, religion, and death. The same directional pattern appears in the distinct words each group uses and in a focused analysis of posts that contain clinical or polarization vocabulary. The paper reads these differences as evidence that partisan culture shapes the idiom of distress, with implications for how online mental health platforms are designed and how people from different partisan backgrounds seek and receive care.

Load-bearing premise

The statistical significance of the comparisons rests on treating each post as an independent observation, even though the same users contribute many posts, so correlated posts from one user could make the reported p-values look more certain than they are.

Editorial extensions

If this is right

  • Online mental health platforms that assume a single style of distress language will serve partisan groups unevenly; offering both clinical and social-relational framings could make support more accessible.
  • Clinical screening tools should not treat psychiatric vocabulary as the only signal of distress, since Republican users' social and somatic language may otherwise be missed.
  • The presence of polarization language inside distress posts indicates that political context is embedded in mental health expression, supporting the view that cultural and political conditions shape idioms of distress.
  • Well-moderated cross-partisan support spaces could use shared distress as a foundation for empathy, translating between clinical and social idioms rather than letting language differences reinforce division.
  • The finding that Republican users' polarization language rose relative to unaffiliated users after 2016 suggests that external political events can shift how distress is expressed online.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the partisan differences reflect cultural framing rather than true symptom differences, then studies of social-media mental health language that do not account for partisan culture may attribute style differences to clinical differences.
  • A direct experimental test, in which partisan users describe the same distress scenario under anonymity, could separate cultural idiom from stigma-driven avoidance of clinical terms.
  • The same matched-comparison method could map partisan culture in other two-party systems, where the party divide may create different idioms of distress.
  • Automated mental-health triage systems trained on clinical vocabulary could be biased against groups that prefer relational or somatic expression, an equity concern the paper raises implicitly.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents a large-scale observational study of 2,184,356 Reddit posts from 8,916 matched Republican, Democrat, and unaffiliated users of online mental health support communities, spanning January 2013 to December 2022. Partisan users are identified using validated subreddit lists and the Rajadesingan et al. method, then matched on platform-use and function-word covariates via Mahalanobis distance and the Hungarian algorithm. The authors analyze posts with LIWC, SAGE keyword extraction, a DSM-derived clinical lexicon, and Simchon et al.'s polarization dictionary, comparing Democrats, Republicans, and unaffiliated users with Welch's t-tests and FDR correction. They report small but highly significant differences: Democrat users use clinical language in 17.75% of posts versus 16.43% for Republican users (p<10^-101), polarization language in 37.10% versus 35.61% (p<10^-79), while Republican users use more social language (12.93% vs. 12.32%, p<10^-163). The paper interprets these differences as evidence that partisan culture shapes expressions of distress online.

Significance. If the statistical claims survive scrutiny, the paper makes a valuable contribution to CSCW and HCI by extending work on cultural idioms of distress to U.S. partisan culture, with concrete design implications for online mental health platforms. Strengths of the study include careful use of external lexicons, explicit FDR correction, detailed supplementary tables, and matching diagnostics (SMD below 0.06, rank correlations above 0.95). The use of published, validated subreddit lists and the transparent reporting of lexicon construction protocols are also commendable. However, the central quantitative claims currently rest on an unsupported independence assumption, and the reported p-values are therefore overstated; the significance of the findings depends on whether they survive a user-clustered reanalysis.

major comments (3)
  1. [3.3 Analytic Approach] The central inferential claim rests on post-level Welch's t-tests that treat each of the 2,184,356 posts as an independent observation. This is not justified: users contribute many posts (e.g., about 166 posts per user in the Democrat-vs-Republican comparison), so the independence assumption is violated and all reported p-values are systematically overstated. For the headline result in §5.1, a 1.32-percentage-point difference (17.75% vs. 16.43%, p<10^-101) would shrink to a t-statistic below 2 under a modest intra-user correlation (e.g., ICC = 0.1), so the difference may no longer be significant at p<0.05 after clustering. I request a reanalysis with user-clustered standard errors, mixed-effects models, or user-level aggregation (e.g., per-user means), with effect sizes and confidence intervals reported alongside p-values. The same issue applies to the incidence comparisons in §5.1 and §6.1 and to the LIWC dimension comparisons in Tables 3, 5, and 7, because they all use the same post-level testing approach.
  2. [5.1 Comparative Utilization of Clinical Language and 6.1 Comparative Utilization of Polarization Language] The paper's emphasis on extremely small p-values obscures the fact that the substantively relevant differences are very small: clinical language incidence differs by 1.32 percentage points and polarization language incidence by 1.49 percentage points between Democrat and Republican users. Because the post-level tests ignore user clustering, these p-values do not convey the true uncertainty of the estimates. Please report standardized effect sizes (e.g., Cohen's d or risk differences with cluster-robust confidence intervals) for the headline comparisons, and interpret the results in terms of practical significance rather than only statistical significance. This is particularly important because the design implications in §7.1 are premised on these differences being robust.
  3. [Supplement G] The temporal analysis in Supplement G inherits the same post-level testing problem as the main analyses, and it adds further multiplicity by splitting the data at 2016 and testing multiple outcomes in each period. The only reversal identified there (Republican vs. Unaffiliated-R polarization incidence post-2016: 35.26% vs. 34.99%, p=0.009) is exactly the kind of small difference that may not survive user-level clustering. Please reanalyze all pre/post comparisons with cluster-robust methods and apply FDR correction across the full set of temporal tests, or clearly label them as exploratory.
minor comments (6)
  1. [1] In the sentence describing the data range, the phrase 'spanning spanning' is duplicated.
  2. [2.1 and throughout] The name 'Heatherington' should be 'Hetherington', and 'Rajadesignan' should be 'Rajadesingan'; these typos appear in multiple places.
  3. [3.3 and tables] The colored-text annotations (brown/red/blue/purple) are not accessible in grayscale print or for color-blind readers; consider adding symbols or table notation in addition to color.
  4. [3.2] The choice of 'top 10' subreddits for expansion of the partisan and mental-health subreddit lists is arbitrary; a brief sensitivity analysis with different thresholds would strengthen construct validity.
  5. [6.1] The sentence 'Democrat users employ more clinical language within their posts' appears in the paragraph on polarization language; this is likely a typo for 'polarization language'.
  6. [Table 3 and Supplement F] Several p-values are reported as below 10^-308, which is beyond standard floating-point precision; please use a notation such as 'p < 10^-300' to avoid implying spurious precision.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical comparisons rest on external lexicons and on matching covariates that are separate from the outcome measures.

full rationale

This is a large-scale observational study; no equation derives its conclusions from its own inputs. Partisan labels come from posting behavior in political subreddits plus karma validation, while the outcome language is measured independently through LIWC, SAGE, a DSM-derived clinical lexicon, and the Simchon et al. polarization dictionary. The matching procedure uses demographic-proxy covariates (subreddit breadth, posts per month, post length, function words) and is separate from the outcome categories, which exclude function words and use externally validated lexicons. The clinical lexicon is filtered from DSM terms via GPT-4 with reported inter-rater reliability, and neither lexicon is fitted to the Democrat-versus-Republican differences that are later tested. SAGE is unsupervised and reports distinct terms; it does not encode the partisan differences as fitted parameters. Self-citations (Pendse et al. 2019, 2023, 2024; Sharma and De Choudhury 2018) are methodological and do not constitute a load-bearing argument: the cited methods are validated in their original venues and are not used to define away the research question. The paper also states its own limitations in Section 7.3, explicitly disclaiming causation and noting sample representativeness concerns, which are scope and statistical-validity caveats rather than circular steps. The post-level Welch t-test independence concern raised by the skeptic is a statistical modeling assumption about clustering; it affects the reliability of p-values but does not make any claim equivalent to its input by construction. Accordingly, no specific circular reduction can be exhibited, and the honest finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The study introduces no new physical or conceptual entities. 'Partisan culture' is a construct from political science literature, and all lexicons are externally sourced or filtered with documented inter-rater reliability. The free parameters are analytic choices (thresholds, lexicon size) rather than fitted model coefficients.

free parameters (3)
  • SAGE top terms count = 15
    Authors report the 15 unigrams with highest SAGE score for each comparison; this choice affects which keywords are highlighted but not the central trend.
  • Subreddit expansion threshold = top 10
    The authors expand partisan and mental health subreddit lists by adding the top 10 subreddits with highest user overlap; this hand-picked threshold influences which users are classified as partisan.
  • Clinical lexicon size = 82 terms
    The filtered DSM-derived lexicon contains 82 terms after GPT-4 filtering; this set defines what counts as clinical language and directly affects the clinical language incidence comparisons.
assumptions (5)
  • domain assumption Posts from the same user are statistically independent
    Welch's t-tests on post-level data implicitly assume independence; multiple posts per user violate this, inflating significance (Section 3.3).
  • domain assumption Partisanship can be inferred from subreddit posting behavior and karma
    Users are classified as Republican or Democrat if they post more in partisan subreddits, have higher karma there, and mean karma > 1, following Rajadesingan et al. (Section 3.2).
  • domain assumption Function word usage is a valid proxy for demographic and personality covariates
    Matching on function word mean and SD is used to control for demographics, based on prior psycholinguistic work (Section 3.2).
  • domain assumption Users who never post in political subreddits are 'unaffiliated'
    The comparison group is defined by absence of political subreddit activity; the authors acknowledge these users may still be partisans offline (Section 7.3).
  • domain assumption LIWC categories and SAGE scores capture psychological and cultural constructs
    The analysis relies on the validity of LIWC dictionaries and the SAGE generative model as measures of language use (Sections 3.3.1 and Supplement D).

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Role of Partisan Culture in Mental Health Language Online." pith.science (2026). https://pith.science/paper/7TK5VEHA

@misc{pith2026250620377,
  author       = {Pith},
  title        = {Pith review of: The Role of Partisan Culture in Mental Health Language Online},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7TK5VEHA}},
  note         = {Machine review of arXiv:2506.20377}
}
read the original abstract

The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.

Figures

Figures reproduced from arXiv: 2506.20377 by the authors.

Figure 1
Figure 1. Between January 2013 to December 2022, 6,335,521 users posted in mental health subreddits. Of those [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. For our analysis, we conduct comparative analyses between matched [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

143 extracted references · 75 canonical work pages

  1. [1]

    Subreddit Stats

    2023. Subreddit Stats. https://subredditstats.com/

    2023

  2. [2]

    American Political Science Association Committee on Political Parties. 2023. More than Red and Blue: Political Parties and American Democracy. Protect Democracy. https://protectdemocracy.org/work/more-than-red-and-blue- political-parties-and-american-democracy/ [Online; accessed 11-July-2023]

    2023

  3. [3]

    2013.Diagnostic and statistical manual of mental disorders: DSM-5

    DSMTF American Psychiatric Association, American Psychiatric Association, et al. 2013.Diagnostic and statistical manual of mental disorders: DSM-5. Vol. 5. American psychiatric association Washington, DC

    2013

  4. [4]

    Jisun An, Haewoon Kwak, Oliver Posegga, and Andreas Jungherr. 2019. Political discussions in homogeneous and cross-cutting communication spaces. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 13. 68–79

    2019

  5. [5]

    Ashwini Ashokkumar and James W Pennebaker. 2022. Tracking group identity through natural language within groups.PNAS nexus1, 2 (2022), pgac022

    2022

  6. [6]

    Lance Betros. 2001. Political partisanship and the military ethic in America.Armed Forces & Society27, 4 (2001), 501–523

    2001

  7. [7]

    Kamaldeep Bhui and Dinesh Bhugra. 2002. Mental illness in Black and Asian ethnic minorities: Pathways to care and outcomes.Advances in Psychiatric Treatment8, 1 (2002), 26–33

    2002

  8. [8]

    Claire Boine, Michael Siegel, Craig Ross, Eric W Fleegler, and Ted Alcorn. 2020. What is gun culture? Cultural variations and trends across the United States.Humanities and Social Sciences Communications7, 1 (2020), 1–12

    2020

Show all 143 references
  1. [9]

    David Broockman and Joshua Kalla. 2016. Durably reducing transphobia: A field experiment on door-to-door canvassing.Science352, 6282 (2016), 220–224

    2016

  2. [10]

    Jacob R Brown and Ryan D Enos. 2021. The measurement of partisan sorting for 180 million voters.Nature Human Behaviour5, 8 (2021), 998–1008

    2021

  3. [11]

    Ira Burnim. 2015. The promise of the Americans with Disabilities Act for people with mental illness.Jama313, 22 (2015), 2223–2224. 24 The Role of Partisan Culture in Mental Health Language Online CSCW’25, October 18–22, 2025, Bergen, Norway

    2015

  4. [12]

    Judith Casant and Marco Helbich. 2022. Inequalities of suicide mortality across urban and rural areas: A literature review.International journal of environmental research and public health19, 5 (2022), 2669

    2022

  5. [13]

    Robert Chew, John Bollenbacher, Michael Wenger, Jessica Speer, and Annice Kim. 2023. LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding.arXiv preprint arXiv:2306.14924(2023)

    2023 arXiv

  6. [14]

    Shaan Chopra, Rachael Zehrung, Tamil Arasu Shanmugam, and Eun Kyoung Choe. 2021. Living with uncertainty and stigma: self-experimentation and support-seeking around polycystic ovary syndrome. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–18

    2021

  7. [15]

    Cindy Chung and James Pennebaker. 2011. The psychological functions of function words. InSocial communication. Psychology Press, 343–359

    2011

  8. [16]

    David L Conley and Melinda J Baum. 2023. Predictors of structural stigma in state mental health legislation during the Trump administration.Social Work in Mental Health21, 1 (2023), 1–27

    2023

  9. [17]

    Joao Couto and Kiran Garimella. 2024. Examining (Political) Content Consumption on Facebook Through Data Donation.arXiv preprint arXiv:2407.08171(2024)

    2024 arXiv

  10. [18]

    Clara Crivellaro, Rob Comber, John Bowers, Peter C Wright, and Patrick Olivier. 2014. A pool of dreams: facebook, politics and the emergence of a social movement. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems. 3573–3582

    2014

  11. [19]

    Shiva Darian, Brianna Dym, and Amy Voida. 2023. Competing Imaginaries and Partisan Divides in the Data Rhetoric of Advocacy Organizations.Proceedings of the ACM on Human-Computer Interaction7, CSCW2 (2023), 1–29

    2023

  12. [20]

    Munmun De Choudhury and Sushovan De. 2014. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. InProceedings of the international AAAI conference on web and social media, Vol. 8. 71–80

    2014

  13. [21]

    Munmun De Choudhury, Shagun Jhaver, Benjamin Sugar, and Ingmar Weber. 2016. Social media participation in an activist movement for racial equality. InProceedings of the international aaai conference on web and social media, Vol. 10. 92–101

    2016

  14. [22]

    Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen Coppersmith, and Mrinal Kumar. 2016. Discovering shifts to suicidal ideation from mental health content in social media. InProceedings of the 2016 CHI conference on human factors in computing systems. 2098–2110

    2016

  15. [23]

    Munmun De Choudhury, Sanket S Sharma, Tomaz Logar, Wouter Eekhout, and René Clausen Nielsen. 2017. Gender and cross-cultural differences in social media disclosures of mental illness. InProceedings of the 2017 ACM conference on computer supported cooperative work and social co...

    2017

  16. [24]

    Kat Devlin, Moira Fagan, and Aidan Connaughton. 2021. People in advanced economies say their society is more divided than before pandemic.Pew Reasearch Center Report, June23 (2021), 2021

    2021

  17. [25]

    Xiaohan Ding, Michael Horning, and Eugenia H Rho. 2023. Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarity in Online Public Discourse. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 17. 161–172

    2023

  18. [26]

    Bryan Dosono and Bryan Semaan. 2020. Decolonizing tactics as collective resilience: Identity work of AAPI commu- nities on Reddit.Proceedings of the ACM on Human-Computer interaction4, CSCW1 (2020), 1–20

    2020

  19. [27]

    Amina Dunn. 2020. Few Trump or Biden Supporters have Close Friends who Back the Opposing Candidate: Pew Research Center

    2020

  20. [28]

    Jacob Eisenstein, Amr Ahmed, and Eric P Xing. 2011. Sparse additive generative models of text. InProceedings of the 28th international conference on machine learning (ICML-11). 1041–1048

    2011

  21. [29]

    Eden Engel-Rebitzer, Daniel C Stokes, Zachary F Meisel, Jonathan Purtle, Rebecca Doyle, and Alison M Buttenheim

  22. [30]

    2002.The Birth of the Grand Old Party: The Republicans’ First Generation

    Robert F Engs and Randall M Miller. 2002.The Birth of the Grand Old Party: The Republicans’ First Generation. University of Pennsylvania Press

    2002

  23. [31]

    Robert S Erikson, John P McIver, and Gerald C Wright. 1987. State political culture and public opinion.American Political Science Review81, 3 (1987), 797–813

    1987

  24. [32]

    Sindhu Kiranmai Ernala, Asra F Rizvi, Michael L Birnbaum, John M Kane, and Munmun De Choudhury. 2017. Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia.Proceedings of the ACM on Human-Computer Interaction1, CSCW (2017), 1–27

    2017

  25. [33]

    Do You Ladies Relate?

    Jessica L Feuston, Michael Ann DeVito, Morgan Klaus Scheuerman, Katy Weathington, Marianna Benitez, Bianca Z Perez, Lucy Sondheim, and Jed R Brubaker. 2022. " Do You Ladies Relate?": Experiences of Gender Diverse People in Online Eating Disorder Communities.Proceedings of the ...

    2022

  26. [34]

    Lucy Foulkes and Jack L Andrews. 2023. Are mental health awareness efforts contributing to the rise in reported mental health problems? A call to test the prevalence inflation hypothesis.New Ideas in Psychology69 (2023), 101010. 25 CSCW’25, October 18–22, 2025, Bergen, Norway ...

    2023

  27. [35]

    Let me tell you about your mental health!

    Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. 2018. " Let me tell you about your mental health!" Contextualized classification of reddit posts to DSM-5 for web-based intervention. InProceedings o...

    2018

  28. [36]

    2004.A human being died that night: A South African woman confronts the legacy of apartheid

    Pumla Gobodo-Madikizela. 2004.A human being died that night: A South African woman confronts the legacy of apartheid. Houghton Mifflin Harcourt

    2004

  29. [37]

    David Goldberg and Peter Huxley. 1980. Mental health in the community: The pathways to psychiatric care.London (UK): Tavistock Publications(1980)

    1980

  30. [38]

    Kristin A Goss. 2015. Defying the odds on gun regulation: The passage of bipartisan mental health laws across the States.American journal of orthopsychiatry85, 3 (2015), 203

    2015

  31. [39]

    2004.Partisan hearts and minds: Political parties and the social identities of voters

    Donald P Green, Bradley Palmquist, and Eric Schickler. 2004.Partisan hearts and minds: Political parties and the social identities of voters. Yale University Press

    2004

  32. [40]

    Orna Guralnik. 2023. I’m a Couples Therapist. Something New Is Happening in Relationships.The New York Times Magazine(16 May 2023). https://www.nytimes.com/2023/05/16/magazine/couples-therapy-orna-guralnik.html

    2023

  33. [41]

    Kevin A Hallgren. 2012. Computing inter-rater reliability for observational data: an overview and tutorial.Tutorials in quantitative methods for psychology8, 1 (2012), 23

    2012

  34. [42]

    Jeff Hemsley, Jennifer Stromer-Galley, Patrícia Rossini, and Alexander Smith. 2021. Staying in Their Lanes: Issue Ownership in the 2016 and 2020 US Presidential Campaigns on Facebook and Twitter.AoIR Selected Papers of Internet Research(2021)

    2021

  35. [43]

    2018.Prius or pickup?: How the answers to four simple questions explain America’s great divide

    Marc Hetherington and Jonathan Weiler. 2018.Prius or pickup?: How the answers to four simple questions explain America’s great divide. Houghton Mifflin

    2018

  36. [44]

    Dan Hiaeshutter-Rice, Fabian G Neuner, and Stuart Soroka. 2023. Cued by culture: Political imagery and partisan evaluations.Political Behavior45, 2 (2023), 741–759

    2023

  37. [45]

    Valentin Hofmann, Hinrich Schütze, and Janet B Pierrehumbert. 2022. The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 16. 1259–1267

    2022

  38. [46]

    Shanto Iyengar, Yphtach Lelkes, Matthew Levendusky, Neil Malhotra, and Sean J Westwood. 2019. The origins and consequences of affective polarization in the United States.Annual Review of Political Science22, 1 (2019), 129–146

    2019

  39. [47]

    Shanto Iyengar and Sean J Westwood. 2015. Fear and loathing across party lines: New evidence on group polarization. American journal of political science59, 3 (2015), 690–707

    2015

  40. [48]

    Joshua L Kalla and David E Broockman. 2020. Reducing exclusionary attitudes through interpersonal conversation: Evidence from three field experiments.American Political Science Review114, 2 (2020), 410–425

    2020

  41. [49]

    Joshua L Kalla and David E Broockman. 2023. Which Narrative Strategies Durably Reduce Prejudice? Evidence from Field and Survey Experiments Supporting the Efficacy of Perspective-Getting.American Journal of Political Science 67, 1 (2023), 185–204

    2023

  42. [50]

    2022.Radical American partisanship: Mapping violent hostility, its causes, and the consequences for democracy

    Nathan P Kalmoe and Lilliana Mason. 2022.Radical American partisanship: Mapping violent hostility, its causes, and the consequences for democracy. University of Chicago Press

    2022

  43. [51]

    2022.What it Took to Win: A History of the Democratic Party

    Michael Kazin. 2022.What it Took to Win: A History of the Democratic Party. Farrar, Straus and Giroux

    2022

  44. [52]

    Ceren Keser, James C Garand, Ping Xu, and Joseph Essig. 2024. Partisanship, Trump favorability, and changes in support for trade.Presidential Studies Quarterly54, 1 (2024), 46–64

    2024

  45. [53]

    2020.The illness narratives: Suffering, healing, and the human condition

    Arthur Kleinman. 2020.The illness narratives: Suffering, healing, and the human condition. Basic books

    2020

  46. [54]

    Arthur Kleinman and Peter Benson. 2006. Anthropology in the clinic: the problem of cultural competency and how to fix it.PLoS medicine3, 10 (2006), e294

    2006

  47. [55]

    Arthur Kleinman, Leon Eisenberg, and Byron Good. 1978. Culture, illness, and care: clinical lessons from anthropologic and cross-cultural research.Annals of internal medicine88, 2 (1978), 251–258

    1978

  48. [56]

    Masha Krupenkin, David Rothschild, Shawndra Hill, and Elad Yom-Tov. 2019. President Trump stress disorder: partisanship, ethnicity, and expressive reporting of mental distress after the 2016 election.Sage open9, 1 (2019), 2158244019830865

    2019

  49. [57]

    I wanted to see how bad it was

    Kaylee Payne Kruzan, Jonah Meyerhoff, Theresa Nguyen, Madhu Reddy, David C Mohr, and Rachel Kornfield. 2022. “I wanted to see how bad it was”: online self-screening as a critical transition point among young adults with common mental health conditions. InProceedings of the 202...

    2022

  50. [58]

    Harold W Kuhn. 1955. The Hungarian method for the assignment problem.Naval research logistics quarterly2, 1-2 (1955), 83–97

    1955

  51. [59]

    Culture wars

    Geoffrey C Layman. 1999. “Culture wars” in the American party system: Religious and cultural change among partisan activists since 1972.American Politics Quarterly27, 1 (1999), 89–121

    1999

  52. [60]

    Guo Li, Xiaomu Zhou, Tun Lu, Jiang Yang, and Ning Gu. 2016. SunForum: understanding depression in a Chinese online community. InProceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social 26 The Role of Partisan Culture in Mental Health Language Onli...

    2016

  53. [61]

    Hanlin Li, Disha Bora, Sagar Salvi, and Erin Brady. 2018. Slacktivists or activists? Identity work in the virtual disability march. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13

    2018

  54. [62]

    Richard J Light. 1971. Measures of response agreement for qualitative data: some generalizations and alternatives. Psychological bulletin76, 5 (1971), 365

    1971

  55. [63]

    Kara Lindaman and Donald P Haider-Markel. 2002. Issue evolution, political parties, and the culture wars.Political research quarterly55, 1 (2002), 91–110

    2002

  56. [64]

    2021.The NewToReddit Encyclopaedia Redditica v2

    llamageddon01. 2021.The NewToReddit Encyclopaedia Redditica v2. https://www.reddit.com/r/NewToReddit/ comments/qbb173/the_newtoreddit_encyclopaedia_redditica_v2/

    2021

  57. [65]

    Michele F Margolis and Michael W Sances. 2017. Partisan differences in nonpartisan activity: The case of charitable giving.Political Behavior39 (2017), 839–864

    2017

  58. [66]

    Annette Markham. 2012. Fabrication as ethical practice: Qualitative inquiry in ambiguous internet contexts.Informa- tion, Communication & Society15, 3 (2012), 334–353

    2012

  59. [67]

    Lilliana Mason. 2018. Losing common ground: Social sorting and polarization. InThe Forum, Vol. 16. De Gruyter, 47–66

    2018

  60. [68]

    2018.Uncivil agreement: How politics became our identity

    Lilliana Mason. 2018.Uncivil agreement: How politics became our identity. University of Chicago Press

    2018

  61. [69]

    2019.The long southern strategy: How chasing white voters in the South changed American politics

    Angie Maxwell and Todd Shields. 2019.The long southern strategy: How chasing white voters in the South changed American politics. Oxford University Press, USA

    2019

  62. [70]

    Jennifer McCoy, Benjamin Press, Murat Somer, and Ozlem Tuncel. 2022. Reducing Pernicious Polarization: A Comparative Historical Analysis of Depolarization. (2022)

    2022

  63. [71]

    John J McGrath, Ali Al-Hamzawi, Jordi Alonso, Yasmin Altwaijri, Laura H Andrade, Evelyn J Bromet, Ronny Bruffaerts, José Miguel Caldas de Almeida, Stephanie Chardoul, Wai Tat Chiu, et al. 2023. Age of onset and cumulative risk of mental disorders: a cross-national analysis of ...

    2023

  64. [72]

    Andrew Mercer, Arnold Lau, and Courtney Kennedy. 2018. For Weighting Online Opt-In Samples, What Matters Most? https://www.pewresearch.org/ Accessed: 2024-10-25

    2018

  65. [73]

    I See Me Here

    Ashlee Milton, Leah Ajmani, Michael Ann DeVito, and Stevie Chancellor. 2023. “I See Me Here”: Mental Health Content, Community, and Algorithmic Curation on TikTok. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17

    2023

  66. [74]

    Nathaniel V Mohatt, Carlee J Kreisel, Adam S Hoffberg, and Sarah J Beehler. 2021. A systematic review of factors impacting suicide risk among rural adults in the United States.The Journal of Rural Health37, 3 (2021), 565–575

    2021

  67. [75]

    Matthew L Newman, Carla J Groom, Lori D Handelman, and James W Pennebaker. 2008. Gender differences in language use: An analysis of 14,000 text samples.Discourse processes45, 3 (2008), 211–236

    2008

  68. [76]

    Mark Nichter. 2010. Idioms of distress revisited.Culture, Medicine, and Psychiatry34 (2010), 401–416

    2010

  69. [77]

    Anne E Norris and Karen J Aroian. 2008. Avoidance symptoms and assessment of posttraumatic stress disorder in Arab immigrant women.Journal of traumatic stress21, 5 (2008), 471–478

    2008

  70. [78]

    Ramon Oldenburg and Dennis Brissett. 1982. The third place.Qualitative sociology5, 4 (1982), 265–284

    1982

  71. [79]

    J Eric Oliver, Thomas Wood, and Alexandra Bass. 2016. Liberellas versus Konservatives: Social status, ideology, and birth names in the United States.Political Behavior38 (2016), 55–81

    2016

  72. [80]

    OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]

    2023 arXiv

  73. [81]

    Kim Parker, J Horowitz, Anna Brown, Richard Fry, D’vera Cohn, and Ruth Igielnik. 2018. Urban, suburban and rural residents’ views on key social and political issues.Pew Research Center(2018)

    2018

  74. [82]

    Kim Parker, Juliana Menasce Horowitz, Ruth Igielnik, J Baxter Oliphant, and Anna Brown. 2017. America’s complex relationship with guns. (2017)

    2017

  75. [83]

    Umashanthi Pavalanathan and Munmun De Choudhury. 2015. Identity management and mental health discourse in social media. InProceedings of the 24th international conference on world wide web. 315–321

    2015

  76. [84]

    Sachin R Pendse, Neha Kumar, and Munmun De Choudhury. 2023. Marginalization and the Construction of Mental Illness Narratives Online: Foregrounding Institutions in Technology-Mediated Care.Proceedings of the ACM on Human-Computer InteractionCSCW (2023)

    2023

  77. [85]

    Sachin R Pendse, Neha Kumar, and Munmun De Choudhury. 2024. Quantifying the Pollan Effect: Investigating the Impact of Emerging Psychiatric Interventions on Online Mental Health Discourse. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–22

    2024

  78. [86]

    Sachin R Pendse, Kate Niederhoffer, and Amit Sharma. 2019. Cross-cultural differences in the use of online mental health support forums.Proceedings of the ACM on Human-Computer Interaction3, CSCW (2019), 1–29

    2019

  79. [87]

    Can I Not Be Suicidal on a Sunday?

    Sachin R Pendse, Amit Sharma, Aditya Vashistha, Munmun De Choudhury, and Neha Kumar. 2021. “Can I Not Be Suicidal on a Sunday?”: Understanding Technology-Mediated Pathways to Mental Health Support. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems....

    2021

  80. [88]

    2015.The development and psychometric properties of LIWC2015

    James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015.The development and psychometric properties of LIWC2015. Technical Report

    2015

  81. [89]

    Pew Research Center. 2024. Changing Partisan Coalitions in a Politically Divided Nation. https://www.pewresearch. org Party identification among registered voters, 1994-2023

    2024

  82. [90]

    Shruti Phadke, Mattia Samory, and Tanushree Mitra. 2021. What makes people join conspiracy communities? role of social factors in conspiracy engagement.Proceedings of the ACM on Human-Computer Interaction4, CSCW3 (2021), 1–30

    2021

  83. [91]

    Yada Pruksachatkun, Sachin R Pendse, and Amit Sharma. 2019. Moments of change: Analyzing peer-based cognitive support in online mental health forums. InProceedings of the 2019 CHI conference on human factors in computing systems. 1–13

    2019

  84. [92]

    2000.Bowling alone: The collapse and revival of American community

    Robert D Putnam. 2000.Bowling alone: The collapse and revival of American community. Simon and schuster

    2000

  85. [93]

    Ashwin Rajadesingan, Ceren Budak, and Paul Resnick. 2021. Political discussion is abundant in non-political subreddits (and less toxic). InProceedings of the International AAAI Conference on Web and Social Media, Vol. 15. 525–536

    2021

  86. [94]

    Afsaneh Razi, Ashwaq AlSoubai, Seunghyun Kim, Nurun Naher, Shiza Ali, Gianluca Stringhini, Munmun De Choud- hury, and Pamela J Wisniewski. 2022. Instagram data donation: a case study on collecting ecologically valid social media data for the purpose of adolescent online risk d...

    2022

  87. [95]

    Eugenia Ha Rim Rho, Gloria Mark, and Melissa Mazmanian. 2018. Fostering civil discourse online: Linguistic behavior in comments of# metoo articles across political perspectives.Proceedings of the ACM on human-computer interaction 2, CSCW (2018), 1–28

    2018

  88. [96]

    Ben Rochford, Sachin Pendse, Neha Kumar, and Munmun De Choudhury. 2023. Leveraging symptom search data to understand disparities in US mental health care: demographic analysis of search engine trace data.JMIR Mental Health10 (2023), e43253

    2023

  89. [97]

    Paul R Rosenbaum and Donald B Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score.The American Statistician39, 1 (1985), 33–38

    1985

  90. [98]

    Social” versus “Political

    Jay Ruckelshaus. 2022. What Kind of Identity is Partisan Identity?“Social” versus “Political” Partisanship in Divided Democracies.American Political Science Review116, 4 (2022), 1477–1489

    2022

  91. [99]

    Koustuv Saha and Munmun De Choudhury. 2017. Modeling stress with social media around incidents of gun violence on college campuses.Proceedings of the ACM on Human-Computer Interaction1, CSCW (2017), 1–27

    2017

  92. [100]

    Laughing so I don’t cry

    Anastasia Schaadhardt, Yue Fu, Cory Gennari Pratt, and Wanda Pratt. 2023. “Laughing so I don’t cry”: How TikTok users employ humor and compassion to connect around psychiatric hospitalization. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–13

    2023

  93. [101]

    Verena Schoenmueller, Oded Netzer, and Florian Stahl. 2022. Polarized America: From political partisanship to preference partisanship.Marketing Science Frontiers Forthcoming(2022)

    2022

  94. [102]

    Philip Schwadel. 2017. The Republicanization of evangelical Protestants in the United States: An examination of the sources of political realignment.Social Science Research62 (2017), 238–254

    2017

  95. [103]

    Tetine Sentell, Martha Shumway, and Lonnie Snowden. 2007. Access to mental health treatment by English language proficiency and race/ethnicity.Journal of general internal medicine22 (2007), 289–293

    2007

  96. [104]

    Richard M Shafranek. 2020. Political consequences of partisan prejudice.Political Psychology41, 1 (2020), 35–51

    2020

  97. [105]

    Eva Sharma and Munmun De Choudhury. 2018. Mental health support and its relationship to linguistic accommodation in online communities. InProceedings of the 2018 CHI conference on human factors in computing systems. 1–13

    2018

  98. [106]

    Eva Sharma, Koustuv Saha, Sindhu Kiranmai Ernala, Sucheta Ghoshal, and Munmun De Choudhury. 2017. Analyzing ideological discourse on social media: A case study of the abortion debate. InProceedings of the 2017 international conference of the computational social science societ...

    2017

  99. [107]

    Yongren Shi, Kai Mast, Ingmar Weber, Agrippa Kellum, and Michael Macy. 2017. Cultural fault lines and political polarization. InProceedings of the 2017 ACM on web science conference. 213–217

    2017

  100. [108]

    Almog Simchon, William J Brady, and Jay J Van Bavel. 2022. Troll and divide: the language of online polarization. PNAS nexus1, 1 (2022), pgac019

    2022

  101. [109]

    Hey, Can You Add Captions?

    Ellen Simpson, Samantha Dalal, and Bryan Semaan. 2023. "Hey, Can You Add Captions?": The Critical Infrastructuring Practices of Neurodiverse People on TikTok.Proceedings of the ACM on Human-Computer Interaction7, CSCW1 (2023), 1–27

    2023

  102. [110]

    C Estelle Smith, Avleen Kaur, Katie Z Gach, Loren Terveen, Mary Jo Kreitzer, and Susan O’Conner-Von. 2021. What is Spiritual Support and How Might It Impact the Design of Online Communities?Proceedings of the ACM on Human-Computer Interaction5, CSCW1 (2021), 1–42

    2021

  103. [111]

    Lonnie R Snowden. 1999. African American folk idiom and mental health services use.Cultural Diversity and Ethnic Minority Psychology5, 4 (1999), 364. 28 The Role of Partisan Culture in Mental Health Language Online CSCW’25, October 18–22, 2025, Bergen, Norway

    1999

  104. [112]

    Ahmed Soliman, Jan Hafer, and Florian Lemmerich. 2019. A characterization of political communities on reddit. In Proceedings of the 30th ACM conference on hypertext and Social Media. 259–263

    2019

  105. [113]

    Charles Soukup. 2006. Computer-mediated communication as a virtual third place: building Oldenburg’s great good places on the world wide web.New media & society8, 3 (2006), 421–440

    2006

  106. [114]

    Elizabeth A Stuart. 2010. Matching methods for causal inference: A review and a look forward.Statistical science: a review journal of the Institute of Mathematical Statistics25, 1 (2010), 1

    2010

  107. [115]

    Karolina Sylwester and Matthew Purver. 2015. Twitter language use reflects psychological differences between democrats and republicans.PloS one10, 9 (2015), e0137422

    2015

  108. [116]

    2022.The Propagandists’ Playbook: How Conservative Elites Manipulate Search and Threaten Democracy

    Francesca Bolla Tripodi. 2022.The Propagandists’ Playbook: How Conservative Elites Manipulate Search and Threaten Democracy. Yale University Press

    2022

  109. [117]

    Amaury Trujillo and Stefano Cresci. 2022. Make reddit great again: assessing community effects of moderation interventions on r/the_donald.Proceedings of the ACM on Human-Computer Interaction6, CSCW2 (2022), 1–28

    2022

  110. [118]

    Emily A Vogels. 2021. Some digital divides persist between rural, urban and suburban America. (2021)

    2021

  111. [119]

    Jacob Wallace, Paul Goldsmith-Pinkham, and Jason L Schwartz. 2023. Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic.JAMA Internal Medicine(2023)

    2023

  112. [120]

    Isaac Waller and Ashton Anderson. 2021. Quantifying social organization and political polarization in online platforms. Nature600, 7888 (2021), 264–268

    2021

  113. [121]

    John T Woolley, Gerhard Peters, and Santa Barbara University Of California. 2023. The American Presidency Project. Web

    2023

  114. [122]

    Volker Wulf, Konstantin Aal, Ibrahim Abu Kteish, Meryem Atam, Kai Schubert, Markus Rohde, George P Yerousis, and David Randall. 2013. Fighting against the wall: Social media use by political activists in a Palestinian village. In Proceedings of the SIGCHI conference on human f...

    2013

  115. [123]

    Renwen Zhang, Jordan Eschler, and Madhu Reddy. 2018. Online support groups for depression in China: Culturally shaped interactions and motivations.Computer Supported Cooperative Work (CSCW)27 (2018), 327–354

    2018

  116. [124]

    Fengxiang Zhao, Fan Yu, Timothy Trull, and Yi Shang. 2023. A New Method Using LLMs for Keypoints Generation in Qualitative Data Analysis. In2023 IEEE Conference on Artificial Intelligence (CAI). IEEE, 333–334

    2023

  117. [125]

    Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023. Synthetic lies: Understanding ai-generated misinformation and evaluating algorithmic and human solutions. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–20

    2023

  118. [126]

    Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2023. Can Large Language Models Transform Computational Social Science?arXiv preprint arXiv:2305.03514(2023)

    2023 arXiv

  119. [127]

    talk”, “friend

    Michael Zoorob. 2019. Blue endorsements matter: How the fraternal order of police contributed to Donald Trump’s victory.PS: Political Science & Politics52, 2 (2019), 243–250. 29 The Role of Partisan Culture in Mental Health Language Online Supplementary Material Supplement A M...

    2019

  120. [129]

    Clem Brooks and Jeff Manza. 1997. Social cleavages and political alignments: US presidential elections, 1960 to 1992. American Sociological Review 62, 6, 937-946

    1997

  121. [130]

    Sharma, Tomaz Logar, Wouter Eekhout, and René Clausen Nielsen

    Munmun De Choudhury, Sanket S. Sharma, Tomaz Logar, Wouter Eekhout, and René Clausen Nielsen. 2017. Gender and cross-cultural differences in social media disclosures of mental illness. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social ...

    2017

  122. [131]

    Steven Greene. 1999. Understanding party identification: A social identity approach. Political Psychology 20, 2, 393-403

    1999

  123. [132]

    Claire Henderson, Sara Evans-Lacko, and Graham Thornicroft. 2013. Mental illness stigma, help seeking, and public health programs. American Journal of Public Health 103, 5, 777-780

    2013

  124. [133]

    S. Y. Jesse and Nick Haslam. 2024. Broad concepts of mental disorder predict self-diagnosis. SSM-Mental Health, Article 100326, 8 pages

    2024

  125. [134]

    Kalmoe and Lilliana Mason

    Nathan P. Kalmoe and Lilliana Mason. 2022. Radical American Partisanship: Mapping Violent Hostility, Its Causes, and the Consequences for Democracy. University of Chicago Press

    2022

  126. [135]

    Ku, Jianheng Li, Cathy Lally, Michael T

    Benson S. Ku, Jianheng Li, Cathy Lally, Michael T. Compton, and Benjamin G. Druss. 2021. Associations between mental health shortage areas and county-level suicide rates among adults aged 25 and older in the USA, 2010 to 2018. General Hospital Psychiatry 70, 44-50

    2021

  127. [136]

    Munsch, Liberty Barnes, and Zachary D

    Christin L. Munsch, Liberty Barnes, and Zachary D. Kline. 2020. Who's to blame? Partisanship, responsibility, and support for mental health treatment. Socius 6, Article 2378023120921652

    2020

  128. [137]

    Mark Nichter. 1981. Idioms of distress: Alternatives in the expression of psychosocial distress: A case study from South India. Culture, Medicine and Psychiatry 5, 4, 379-408

    1981

  129. [138]

    Pendse, Kate Niederhoffer, and Amit Sharma

    Sachin R. Pendse, Kate Niederhoffer, and Amit Sharma. 2019. Cross-cultural differences in the use of online mental health support forums. Proceedings of the ACM on Human-Computer Interaction 3, CSCW, Article 67, 29 pages. https://doi.org/10.1145/3359169

    2019 doi

  130. [139]

    Pendse, Neha Kumar, and Munmun De Choudhury

    Sachin R. Pendse, Neha Kumar, and Munmun De Choudhury. 2023. Marginalization and the construction of mental illness narratives online: foregrounding institutions in technology-mediated care. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2, Article 300, 30 pages. ...

    2023 doi

  131. [140]

    Pennebaker, Ryan L

    James W. Pennebaker, Ryan L. Boyd, Kayla Jordan, and Kate Blackburn. 2015. The development and psychometric properties of LIWC2015. Technical Report. University of Texas at Austin

    2015

  132. [141]

    Pew Research Center. 2024. Changing Partisan Coalitions in a Politically Divided Nation: Partisanship in Rural, Suburban, and Urban Communities. Technical Report. Pew Research Center, Washington, DC

    2024

  133. [142]

    Roberts and Stephen M

    Damon C. Roberts and Stephen M. Utych. 2020. Linking gender, language, and partisanship: Developing a database of masculine and feminine words. Political Research Quarterly 73, 1, 40-50

    2020

  134. [143]

    Philip Schwadel. 2017. The Republicanization of evangelical Protestants in the United States: An examination of the sources of political realignment. Social Science Research 62, 238-254. The Role of Partisan Culture in Mental Health Language Online Supplementary Material Suppl...

    2017

  135. [2022]

    Partisan differences in legislators’ discussion of vaccination on Twitter during the COVID-19 era: natural language processing analysis.Jmir Infodemiology2, 1 (2022), e32372

    2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.