Toxicity and U.S. Senate candidate Twitter messaging in the 2022 election

Abstract

Social media has had a transformative effect on politics and governance. While research into the consequences of social media use on politics and governance is mixed, elected officials and political candidates commonly use multiple social media channels to grow constituencies and draw attention to their agenda. A well-crafted social media strategy can further the perception of a candidate or elected official as credible, trustworthy, and able to effectively govern. However, research into social media has suggested that engagement is often driven by toxic and negative content, limiting the possibility of productive political discourse. Using a dataset of posts from 58 U.S. Senate candidates on Twitter, this study seeks to determine whether social media-based political communication during the 2022 election aligns with preexisting perceptions of toxicity. The results suggest that the toxicity of candidates’ Twitter-based political communication is mostly low, though temporal and group differences were found. This case study provides a basis for further investigation into candidates’ strategic use of social media.

Keywords

political communication politics senate twitter toxicity social media

Intoduction

Evidence suggests that many citizens use social media as a primary source of political information. This trend has not gone unnoticed by political communication scholars, as online political communication has long been an area of investigation (Rossini, 2020). The increasing consumption of political information via social media has led to fears that citizens only receive information consistent with their existing beliefs. Much of this information comes from media outlets and/or candidate surrogates rather than the candidates themselves. This situation, coupled with the rise of “bots” and the common presence of misinformation, calls into question whether political communication via social media is a positive force.

As a semi-direct line of communication between themselves and the electorate, candidates use social media to bypass traditional media outlets and communicate their message. Questions remain, however, about whether candidates’ digital communications follow the toxic patterns often associated with social media dialogue and whether those communications change over time. This study explores the Twitter feeds of 2022 U.S. Senate candidates to determine toxicity patterns associated with social media-based political communication. The brevity of social media messaging means that candidates must choose their words with an eye toward voter engagement; as prior research (e.g., Rafail et al., 2024; Rathje et al., 2021) has shown, social media messaging involving more negative, emotional, and moral content is more likely to be engaging. Candidates may therefore be compelled to craft messages that inflame rather than educate. This case study explores whether the content of social media-based political communication aligns with preexisting perceptions of toxicity.

Social media use in electoral politics

Social media has had a transformative effect on governance and politics. As a critical component of e-government, social media can be used as a tool to build trust and confidence in both government institutions and those who govern (Park et al., 2016; Warren et al., 2014). While research into the consequences of social media use on politics and governance is mixed, elected officials and candidates commonly use multiple social media channels to grow their constituencies and draw attention to themselves and their agenda. Prior research exploring the link between candidates’ social media use and electoral outcomes has shown it to be a complex, nuanced relationship (Afonso et al., 2025).

Social media offers politicians the ability to influence the political agenda independent of traditional media outlets. Traditional media, once the gatekeeper of the political agenda, has in many ways been supplanted by the hybrid media systems created by the rise of the Internet and social media (Chadwick, 2017; Jungherr et al., 2019). As a freely available tool for promotion, social media allows politicians to define the issues that matter to them and to keep them in the public consciousness. Prior research has shown the impact of social media on issue salience and political reach (Fazekas et al., 2021; Feezell, 2018).

Social media content allows politicians to shape their own image through unfiltered communication with followers. Acting as a “parasocial proxy for direction interaction” (Carr, 2020: 2), social media can promote greater engagement and greater perceptions of electability. A well-crafted social media strategy can further the perception of a candidate or elected official as credible, trustworthy, and able to effectively govern. Higher perceptions of information source credibility have been shown to promote the chances of electoral success (Housholder and LaMarre, 2014). Candidates’ attempts at using social media to construct a positive “brand” may be differentiated by gender and/or other characteristics. For example, an exploration of tweets from 2020 to 2022 U.S. congressional candidates found men and women presented themselves differently in terms of tone and content, and that party affiliation revealed intragender differences (Horan et al., 2024).

Social media and toxicity

Social media engagement has been shown to be largely driven by out-group animosity and/or the expression of moral outrage, providing politicians with a rationale for sharing more negative content. Research into content features which promote social media engagement have shown that content which stokes emotions and/or includes moral and emotional content are more likely to be shared within the in-group (Brady et al., 2017; Rathje et al., 2021). Recent research by Rafail et al. (2024) found that social media can create a feedback loop of sorts, where polarizing or extreme content triggers greater engagement, which in turn fosters a greater amount of this content. Morality is central to human social behavior, and social media can serve to amplify negative aspects of morality (e.g., harassment) as well as positive aspects (e.g., mobilizing collective action for social good). Expressing moral outrage can signal group affiliation (Van Bavel et al., 2024). This encourages users—including politicians and political organizations—to utilize such content to gather a large following and promote their agenda.

Besides moral outrage, negativity alone can be used to strengthen partisan identity. Exposure to excessively negative content about the in-group can promote polarization by fostering anger toward the opposing side (Rathje et al., 2021). In negating the out-group, in-group members enhance their own identity through differentiation; in the political sense, identity-driven communication serves to build hostility between people on opposing sides (Wojcieszak et al., 2022). This perception aligns with social psychology research suggesting that stronger identification with one group is often paired with more negative perceptions of the opposing group (Nordbrandt, 2021). The strong level of partisanship in the United States may also drive these phenomena on social media. U.S. partisanship has previously been called a “mega identity” along with other identities such as race, structure, citizens, and social networks (Mason, 2018). This identity is said to be aligned with U.S. media consumption—including social media—which in turn helps foster polarization, partisanship, and mega identities (Lane et al., 2025). The unique nature of the U.S. political-media ecosystem offers potential for gaining meaningful insight into modern political communication and political identity, even though the insights and results may not be immediately transferable to other contexts.

Increasing political animosity in the United States and abroad has been linked to incivility toward political opponents (Mamakos and Finkel, 2023) with uncivil attitudes especially prominent among those identifying as strongly partisan (Miller and Conover, 2015). Concerns over civility in online political discourse often focuses on toxic political comments, broadly defined by Kim et al. (2021) as “expressing disrespect for someone by using insulting language, profanity, or name-calling; by engaging in personal attacks; and/or by employing racist, sexist, and xenophobic terms” (p. 924). As mutual respect is a natural precursor to productive political discussion between parties, maintaining civility and adhering to group norms in online discourse is considered necessary to prevent discussions from devolving into a toxic exchange (Cheng et al., 2017; Shmargad et al., 2022). Prior research has confirmed the existence of toxicity in online discussions (e.g., Chen, 2017; Ventura et al., 2021) but researchers have drawn varied conclusions on the severity of this phenomena (Kim et al., 2021; Rossini, 2020).

This study explores the political communication of major party U.S. Senate candidates during the height of the 2022 election to uncover patterns associated with toxicity. The first research question focuses on how the level of toxicity changes during the 100-day period leading up to election day. Temporal changes were expected as negative campaigning is a more common strategy for candidates who trail in the polls (Auter and Fine, 2016), and it is reasonable to assume candidates may become more desperate as the campaign nears completion:

RQ1: What was the toxicity of Twitter posts by U.S. Senate candidates, and how did that change over the course of election season?

This study will also explore gender differences in candidates’ social media content. Prior research by Haferkamp et al. (2012), Horan et al. (2024), and others has suggested gender-based differences in candidates’ social media presentation strategies:

RQ2: What differences in the toxicity of Twitter posts by U.S. Senate candidates exist based on candidate gender (male or female)?

As toxicity has associated with fostering partisan identity, this study will investigate toxicity differences based on party affiliation:

RQ3: What differences in the toxicity of Twitter posts by U.S. Senate candidates exist based on party affiliation (Republican or Democrat)?

This study will also investigate toxicity differences based on incumbent status, as this has been identified as an under-researched variable which can impact candidates’ social media strategies (Afonso et al., 2025). Incumbent candidates often possess greater name recognition and visibility, whereas non-incumbents must work to draw attention to themselves and their policies:

RQ4: What differences in the toxicity of Twitter posts by U.S. Senate candidates exist based on incumbent status?

Methods

The study sample involves major party U.S. Senate candidates in 28 states, comprised of 30 Republican and 28 Democratic candidates. Twitter was used as the social media platform for investigation due to its popularity; Twitter (now X) counts approximately one-quarter of American adults as users (Gottfried, 2024). Twitter/X is also rife with political content; about one-third of all tweets and retweets discuss political issues (Bestvater et al., 2022). Data from the 58 candidates’ Twitter accounts was collected using public APIs via custom Python scripts. Data collection occurred for the 100-day period beginning August 1, 2022, and ending on November 8, 2022. This period was chosen due to it being the post-primary season, when final party candidates have been selected and voters focus on electing individual senators. Data collection for additional Senate candidates were originally planned but was not possible after free access to tweet data was removed by Twitter/X in mid-2023. The sample dataset was comprised of 23,022 tweets (13,234 from Democrats, 9788 from Republicans). See Table 1 for the candidates and their screen names. See Table 2 for descriptive statistics about the dataset.

Table 1.

Candidates included in data collection.

State	Candidate name	Party	Twitter handle
AK	Lisa Murowski*	Republican	Lisamurkowski
AK	Patricia Chesbro	Democrat	ChooseChesbro
AK	Kelly Tshibaka	Republican	KellyForAlaska
AR	John Boozman*	Republican	JohnBoozman
AR	Natalie James	Democrat	JamesforAR
AZ	Mark Kelly*	Democrat	SenMarkKelly
AZ	Blake Masters	Republican	Bgmasters
CA	Alex Padilla*	Democrat	SenAlexPadilla
CA	Mark Meuser	Republican	MarkMeuser
CO	Michael Bennet*	Democrat	SenatorBennet
CO	Joe O'Dea	Republican	OdeaForColorado
CT	Richard Blumenthal*	Democrat	SenBlumenthal
CT	Leora Levy	Republican	LeoraLevyCT
FL	Marco Rubio*	Republican	SenRubioPress
FL	Val Demings	Democrat	RepValDemings
GA	Raphael Warnock*	Democrat	SenatorWarnock
GA	Herschel Walker	Republican	TeamHerschel
IA	Chuck Grassley*	Republican	ChuckGrassley
IA	Michael Franken	Republican	FrankenforIowa
ID	Mike Crapo*	Republican	MikeCrapo
ID	David Roth	Democrat	rothforIdaho
IL	Tammy Duckworth*	Democrat	SenDuckworth
IL	Kathy Salvi	Republican	Salviforsenate
IN	Todd young*	Republican	SenToddYoung
IN	Thomas McDermott	Democrat	Gomcdermott
KS	Jerry Moran*	Republican	JerryMoran
KS	Mark Holland	Democrat	Holland4Kansas
KY	Rand Paul*	Republican	SenRandPaul
KY	Charles Booker	Democrat	Booker4KY
LA	John Kennedy*	Republican	SenJohnKennedy
LA	Gary Chambers	Democrat	GaryChambersJr
LA	Luke Mixon	Democrat	LukeMixonLA
MD	Chris Van Hollen*	Democrat	ChrisVanHollen
MD	Chris Chaffee	Republican	ChaffeeUSSenate
MO	Eric Schmitt	Republican	AGEricSchmitt
MO	Trudy Valentine	Democrat	Buschvalentine
NC	Cheri Lynn Beasley	Democrat	CheriBeasleyNC
NC	Tedd Budd	Republican	RepTedBudd
ND	John Hoeven*	Republican	SenJohnHoeven
ND	Katrina Christiansen	Democrat	katrina_senate
NV	Catherine Cortez Masto*	Democrat	SenCortezMasto
NV	Adam Laxalt	Republican	AdamLaxalt
NY	Chuck Schumer*	Democrat	SenSchumer
NY	Joe Pinion	Republican	JosephPinion
OH	Tim Ryan	Democrat	RepTimRyan
OH	J.D. Vance	Republican	JDVance1
OK	James Lankford*	Republican	SenatorLankford
OK	Madison Horn	Democrat	Madisonhornok
OR	Ron Wyden*	Democrat	RonWyden
OR	Jo Rae Perkins	Republican	PerkinsForUSSen
PA	Mehmet Oz	Republican	DrOz
PA	John Fetterman	Democrat	JohnFetterman
SD	John Thune*	Republican	SenJohnThune
SD	Brian Bengs	Democrat	BrianBengs
VT	Peter Welch	Democrat	PeterWelch
VT	Gerald Malloy	Republican	GeraldMalloyVT
WA	Patty Murray*	Democrat	PattyMurray
WA	Tiffany Smiley	Republican	SmileyForWA

*Incumbent.

Table 2.

Descriptive statistics for full dataset.

	Overall	Republican	Democrat
	Overall	N (Tweet N)	N (Tweet N)
Tweets	23,022	9788	13,234
Election result
Win	28	15 (3481)	13 (5050)
Loss	30	15 (6307)	15 (8184)
Gender
Male	42	18 (8228)	24 (8187)
Female	16	10 (1560)	6 (5047)
Incumbent
Yes	23	12 (2933)	11 (3423)
No	35	18 (6855)	17 (9811)

This investigation used the Perspective API to measure the toxicity for each tweet. This API has been used extensively in research to assess the toxicity of written communications (e.g., Mamakos and Finkel, 2023). Analysis of text data by the Perspective API produced a predefined toxicity attribute for each tweet. Scores range from 0.0 to 1.0, with scores representing a probability that the text in question will be perceived as toxic by readers (Perspective, nd). Scores above 0.7 are thought to represent “high” scores (more likely to be perceived as toxic), and those less than 0.3 representing “low” (less likely to be perceived as toxic) scores (Pascual-Ferrá et al., 2021). Analysis utilized ANOVA and Independent Samples t-Tests for group and temporal differences.

Results

Toxicity of U.S. Senate candidate posts

Research question RQ1 focused on the toxicity of Twitter posts by U.S. Senate candidates over the 100-day period. See Table 3 for descriptive statistics. The results show toxicity was generally low among Senate candidate tweets, though with temporal differences. Welch’s ANOVA results showed that toxicity varied significantly between months, F_w (3, 9038.983) = 4.354, p < 0.01. Post-hoc comparisons using the Games-Howell test showed significant differences in toxicity between tweets from August (mean = 0.052, SD = 0.079) compared to September (mean = 0.056, SD = 0.082, p < 0.01) and October (mean = 0.056, SD = 0.082, p < 0.05). These results suggest that tweets from early stages of the campaign were less likely to be perceived as toxic than in later stages.

Table 3.

Descriptive statistics for toxicity.

	Tweets	Toxicity
	Tweets	Mean (SD)
Overall	23,022	0.055 (0.081)
Political Party
Democrats	13,234	0.060 (0.083)
Republicans	9788	0.051 (0.079)
Incumbent
Yes	6356	0.036 (0.054)
No	16,666	0.062 (0.087)
Gender
Male	16,415	0.058 (0.085)
Female	6607	0.045 (0.068)
Month
August	7185	0.052 (0.079)
September	6629	0.056 (0.082)
October	6840	0.056 (0.082)
November*	2368	0.055 (0.079)

*Partial month.

The Independent Samples t-Tests for gender differences (RQ2) results showed that tweets from male candidates (mean = 0.058, SD = 0.085) had statistically significantly higher overall toxicity scores compared to those from female candidates (mean = 0.045, SD = 0.068), t (14,998.008) = 12.333, p < 0.001, Cohen’s d = −0.164. In terms of political differentiation (RQ3), tweets from Republican candidates (mean = 0.060, SD = 0.083) had significantly higher overall toxicity scores compared to those from Democratic candidates (mean = 0.051, SD = 0.079), t (20,412.192) = 8.175, p < 0.001, Cohen’s d = 0.110. In terms of incumbent status (RQ4) tweets from non-incumbent candidates (mean = 0.062, SD = 0.087) had significantly higher toxicity scores compared to those from incumbents (mean = 0.036, SD = 0.054), t (18,286.208) = 27.441, p < 0.001, Cohen’s d = 0.331. These results show that tweets from males, Republicans, and non-incumbents were more likely to be perceived as toxic than tweets from females, Democratic candidates, and incumbents, respectively. Effect sizes in all cases were small.

Discussion

The analysis of Twitter/X communication during the 2022 U.S. Senate election season (August–November) indicates that candidate posts exhibited little of the toxic character so often associated with social media. The results suggest that, overall, U.S Senate candidate posts during this period were generally low in toxicity. Over the course election season, tweets from later stages of the campaign were significantly more likely to be perceived as toxic than earlier tweets, perhaps suggesting a more intentional approach among some candidates closer to election day. Tweets from female candidates, Democrats, and incumbents were all found to be significantly less likely to be perceived as toxic than tweets from male candidates, Republicans, and non-incumbents, though effect sizes were small. It is possible that the group differences found in this study may be influenced by the demographics of the platform; prior research has shown that Twitter/X users have tended to be younger and more politically liberal (Bestvater et al., 2022). As a strategic communication tool, focusing Twitter/X messaging toward its audience would be logical, though it is not uncommon to find identical messages broadcast across different social media platforms. Future research should delve into whether politicians utilize differential, targeted messaging based on the predominant demographics of the platform.

The relatively limited toxic content stands in contrast to existing research which has shown that the use of moral and emotional content, especially expressions of outrage, results in greater sharing and engagement. Though this study does not assess engagement, the results show that tweets from these candidates exhibit limited amounts of toxic content overall. This generally neutral messaging may indicate that traditional forms of social media engagement are not a primary consideration for the campaigns. Further research is necessary to determine the link between traditional engagement measures and tweet content. Research into subsequent threaded discussions (i.e., replies) may provide clarity on whether candidates’ social media communications promote meaningful discourse or simply devolve into partisan toxicity.

The acquisition of Twitter by Elon Musk in late 2022 brought with it a number of platform changes, including a shift in content moderation away from a human-based system to one more reliant on both community-driven moderation and algorithmic/AI filtering of false or potentially harmful content. This policy change has necessarily impacted the discussion dynamics of Twitter/X conversations in general, and it is reasonable to assume that political communication on the platform has been impacted as well, especially given the evolution of the U.S. political landscape. As a result, the present study can be considered a point-in-time case study of social media-based U.S. political communication. Future research should compare long-term differences in the toxicity of candidate communications between the current environment and the pre-2022 time period. As mentioned previously, the focus on the U.S. as a case study may not provide results immediately transferable to other national contexts. Future comparative research in international contexts would help to more clearly identify whether common patterns of toxic political communication exist.

The dataset is the primary limitation of this study. The end of academic research access to Twitter content in mid-2023 resulted in a limited dataset; additional candidates were intended to be part of the sample. Future research in this area will require significant funding resources and/or free access to the data found on social media platforms.

Conclusion

This study explored the Twitter (now X) feeds of 2022 U.S. Senate candidates to determine patterns of toxicity. The results suggest that these candidates’ Twitter/X-based political communication is mostly neutral, though group differences were found based on political traits (e.g., political party and incumbent status). This study provides a basis for further investigation into the nature of candidates’ social media-based communications. Future research should incorporate more international, comparative, platform-specific, and long-term analysis techniques to uncover whether toxicity patterns exist in political candidates’ social media communications.

Footnotes

ORCID iD

Jeffrey A. Stone

Ethical considerations

The methodology for this study was deemed exempt by the Office for Research Protection of Penn State University. The Office for Research Protections determined that the proposed activity, as described in this submission, does not meet the definition of human subject research as defined in 45 CFR 46.102(e) and/or (l).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The dataset(s) generated during and/or analyzed during the current study are not publicly available due to Twitter licensing policies prohibiting distribution of large numbers of tweets but are available from the corresponding author on reasonable request.*

The tweet texts analyzed during this study are not available in the provided repository (https://doi.org/10.17605/OSF.IO/6CDPK) due to Twitter/X licensing policies prohibiting distribution. The repository includes the study data necessary to replicate all statistical analyses for this study. The repository also includes the numerical ID of each tweet analyzed. The tweets IDs may be used by interested parties to retrieve the full tweet text content via the Twitter/X API.

Carnegie Corporation of New York Grant

This publication was made possible (in part) by a grant from the Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.

Supplemental Material

Supplemental material for this article is available online.

The replication files are available at:

References

Afonso

Rita

António

(2025) Using candidates’ tweets to predict an election outcome. Political Research Quarterly 78(1): 323–340.

Auter

Fine

(2016) Negative campaigning in the social media age: attack advertising on Facebook. Political Behavior 38(4): 999–1020.

Bestvater

Shah

Rivero

, et al. (2022) Politics on Twitter: One-Third of Tweets from U.S. Adults are Political. Pew Research Center - U.S. Politics & Policy. https://www.pewresearch.org/politics/2022/06/16/politics-on-twitter-one-third-of-tweets-from-u-s-adults-are-political/ (Accessed 01 August 2024).

Brady

Wills

Jost

, et al. (2017) Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences of the United States of America 114(28): 7313–7318.

Carr

(2020) The delocalization of the local election. Social Media + Society 6(2): 2056305120924772.

Chadwick

(2017) The Hybrid Media System: Politics and Power. Oxford University Press.

Chen

(2017) Online Incivility and Public Debate: Nasty Talk. Springer.

Cheng

Bernstein

Danescu-Niculescu-Mizil

, et al. (2017) Anyone can become a troll: causes of trolling behavior in online discussions. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, New York, NY, 1217–1230.

Fazekas

Popa

Schmitt

, et al. (2021) Elite‐public interaction on Twitter: EU issue expansion in the campaign. European Journal of Political Research 60(2): 376–396.

10.

Feezell

(2018) Agenda setting through social media: the importance of incidental news exposure and social filtering in the digital era. Political Research Quarterly 71(2): 482–494.

11.

Gottfried

(2024) Americans’ Social Media Use. Pew Research Center; Pew Research Center. https://www.pewresearch.org/internet/2024/01/31/americans-social-media-use/ (Accessed 01 August 2024).

12.

Haferkamp

Eimler

Papadakis

, et al. (2012) Men are from Mars, women are from Venus? Examining gender differences in self-presentation on social networking sites. Cyberpsychology, Behavior, and Social Networking 15(2): 91–98.

13.

Horan

Brubaker

King

, et al. (2024) Tweet like trump: political branding and Twitter usage among congressional candidates in 2020 and 2022. Social Science Quarterly 105(5): 1806–1820.

14.

Housholder

LaMarre

(2014) Facebook politics: toward a process model for achieving political source credibility through social media. Journal of Information Technology & Politics 11(4): 368–382.

15.

Jungherr

Posegga

(2019) Discursive power in contemporary media systems: a comparative framework. The International Journal of Press/Politics 24(4): 404–425.

16.

Kim

Guess

Nyhan

, et al. (2021) The distorting prism of social media: how self-selection and exposure to incivility fuel online comment toxicity. Journal of Communication 71(6): 922–946.

17.

Lane

Chen

Wang

(2025) An “Identity Turn” in political communication? testing the relationship between media use and identity alignment in the United States. Journal of Communication: jqaf026. https://doi.org/10.1093/joc/jqaf026

18.

Mamakos

Finkel

(2023) The social media discourse of engaged partisans is toxic even when politics are irrelevant. PNAS Nexus 2(10): pgad325.

19.

Mason

(2018) Uncivil Agreement: How Politics Became our Identity. University of Chicago Press.

20.

Miller

Conover

(2015) Red and blue states of mind: partisan hostility and voting in the United States. Political Research Quarterly 68(2): 225–239.

21.

Nordbrandt

(2021) Affective polarization in the digital age: testing the direction of the relationship between social media and users’ feelings for out-group parties. New Media & Society 25(12): 3392–3411.

22.

Park

Kang

Rho

, et al. (2016) Policy role of social media in developing public trust: twitter communication with government leaders. Public Management Review 18(9): 1265–1288.

23.

Pascual-Ferrá

Alperstein

Barnett

, et al. (2021) Toxicity and verbal aggression on social media: polarized discourse on wearing face masks during the COVID-19 pandemic. Big Data & Society 8(1): 20539517211023533.

24.

Perspective (n.d.) Developers: Model Cards. Perspective. https://developers.perspectiveapi.com/s/about-the-api-model-cards?language=en_US&tabset-20254=2 (Accessed 01 August 2024).

25.

Rafail

O’Connell

Sager

(2024) Polarizing feedback loops on Twitter: congressional tweets during the 2022 midterm elections. Socius 10: 23780231241228924.

26.

Rathje

Van Bavel

Van Der Linden

(2021) Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences of the United States of America 118(26): e2024292118.

27.

Rossini

(2020). “Chapter 14: Beyond toxicity in the online public sphere: understanding incivility in online political talk”. In A Research Agenda for Digital Politics. Cheltenham, UK: Edward Elgar Publishing. pp. 160-170.

28.

Shmargad

Coe

Kenski

, et al. (2022) Social norms and the dynamics of online incivility. Social Science Computer Review 40(3): 717–735.

29.

Van Bavel

Robertson

Del Rosario

, et al. (2024) Social media and morality. Annual Review of Psychology 75(1): 311–340.

30.

Ventura

Munger

McCabe

, et al. (2021) Connective effervescence and streaming chat during political debates. Journal of Quantitative Description: Digital Media 1: 1–49.

31.

Warren

Sulaiman

Jaafar

(2014) Social media effects on fostering online civic engagement and building citizen trust and trust in institutions. Government Information Quarterly 31(2): 291–301.

32.

Wojcieszak

Sobkowicz

, et al. (2022) What information drives political polarization? Comparing the effects of in-group praise, out-group derogation, and evidence-based communications on polarization. The International Journal of Press/Politics 27(2): 325–352.