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Negativity and Elite Message Diffusion on Social Media

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Abstract

Social media has become a common feature in American politics, with more frequent use among the masses and elites alike. With this increased salience, researchers have explored various aspects of social media use and its impact on political outcomes. While we know a great deal about elite adoption and use of social media platforms, we know comparatively less about why some of these social media messages ‘go viral,’ while others receive little to no attention. Drawing on research from the political science literature on emotional appeals, as well as work in marketing and psychology, we argue that elite messages will spread when they contain strong emotional language. Using both human and automated coding of senators’ tweets, we demonstrate that elite messages that are more negative and those that contain political attacks are more likely to spread on social media. Our findings suggest that politicians have an incentive to engage in more negativity online, which might further increase affective polarization in American politics.

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Data Availability

The data and the code necessary to replicate the models in our tables are available on the Political Behavior Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IEPB4B.

Notes

  1. Twitter’s API limits the process to the most recent 3200 tweets.

  2. Senators’ Tweets are likely shared by both actual users and automated ‘bot’ accounts. While we are unaware of a way to remove retweets from bots, we also have no reason to expect that bot accounts would be more likely to retweet messages based on their content. They therefore are unlikely to be a source of systematic bias affecting our results.

  3. We have 94.75% agreement, with an expected agreement level of 61.98%. The value of the kappa statistic is 0.86 with a standard error of 0.02 (z = 37.9, Prob. > z = 0.00).

  4. The two measures are correlated at − 0.38, significant at the 0.0001 level.

  5. As with the Sentiment score, we perform a series of validity checks on each of the emotion scores, comparing them with our own hand coding of a subset of 344 randomly selected Tweets. Each emotion classification performs well overall. The results of these tests, as well as the interrater reliability scores for our hand coding of emotion, are available upon request.

  6. In results not reported, we have run additional models including a control variable capturing the age of the Tweet (the number of days since the Tweet was generated). Our main results are unchanged in sign and signifncance when this alternate specification is used, though the legislator ideology variable is significant at the 0.05 level when we control for this variable.

  7. In results not reported, we re-run each of our full sample models with an additional variable that captures the age of the Tweet in days. Our main findings are substantively the same as those reported here, both in terms of sign and significance. In these additional models, the age of the Tweet is statistically significant and negative (more recent messages are retweeted more), and our member ideology variable also becomes significant at the 0.05 level.

References

  • Abramowitz, A. I., & Webster, S. W. (2016). The rise of negative partisanship and the Nationalization of US Elections in the 21st century. Electoral Studies, 41(1), 12–22.

    Article  Google Scholar 

  • Abramowitz, A. I., & Webster, S. W. (2018). Negative partisanship: Why Americans dislike parties but behave like rabid partisans. Political Psychology, 39(S1), 119–135.

    Article  Google Scholar 

  • Auter, Z. J., & Fine, J. A. (2016). Negative campaigning in the social media age: Attack advertising on facebook. Political Behavior, 38(4), 999–1020.

    Article  Google Scholar 

  • Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political Analysis, 23(1), 76–91.

    Article  Google Scholar 

  • Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370.

    Article  Google Scholar 

  • Berger, J. (2011). Arousal increases social transmission of information. Psychological Science, 22(7), 891–893.

    Article  Google Scholar 

  • Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.

    Article  Google Scholar 

  • Bode, L., Lassen, D. S., Kim, Y. M., Shah, D. V., Fowler, E. F., Ridout, T. N., & Franz, M. M. (2016). Coherent campaigns? Campaign broadcast and social messaging. Online Information Review, 40(5), 580–594.

    Article  Google Scholar 

  • Brader, T. (2005). Striking a responsive chord: How political ads motivate and persuade voters by appealing to emotions. American Journal of Political Science, 49(2), 388–405.

    Article  Google Scholar 

  • Brader, T. (2006). Campaigning for hearts and minds: How emotional appeals in political ads work. University of Chicago Press.

    Google Scholar 

  • Chen, Z., & Berger, J. (2013). When, why, and how controversy causes conversation. Journal of Consumer Research, 40(3), 580–593.

    Article  Google Scholar 

  • Eckler, P., & Bolls, P. (2011). Spreading the virus: Emotional tone of viral advertising and its effect on forwarding intentions and attitudes. Journal of Interactive Advertising, 11(2), 1–11.

    Article  Google Scholar 

  • Edwards, G. C. (2015). Staying private. In N. Persily (Ed.), Solutions to polarization. Cambridge University Press.

    Google Scholar 

  • Evans, H. K., Cordova, V., & Sipole, S. (2014). Twitter style: An analysis of how house candidates used twitter in their 2012 campaigns. PS: Political Science and Politics, 47(2), 454–62.

    Google Scholar 

  • Evans, H. K., Green, S., & Ovalle, J. (2016). Rockin’ Robins: Do congresswomen rule the roost on the twittersphere. Journal of the Association for Information Science and Technology, 67(2), 268–275.

    Article  Google Scholar 

  • Fandos, N., & Isaac, M. (2016). C-span delivers on sit-in, even with cameras off. New York Times.

  • Finkel, S. E., & Geer, J. G. (1998). A spot check: Casting doubt on the demobilizing effect of attack advertising. American Journal of Political Science, 42(2), 573–595.

    Article  Google Scholar 

  • Fournier, P., Soroka, S. N., & Nir, L. (2020). Negativity biases and political ideology: A comparative test across 17 countries. American Political Science Review, 114(3), 775–791.

    Article  Google Scholar 

  • Gainous, Jn., & Wagner, K. M. (2014). Tweeting to power: The social media revolution in American politics. Oxford University Press.

    Google Scholar 

  • Galloway, S., & Guthrie, D. (2010). “Digital IQ Index: The Senate.” New York: L2 Think Tank for Digital Innovation.

  • Geer, J. G. (2006). In defense of negativity: Attack ads in presidential campaigns. University of Chicago Press.

    Book  Google Scholar 

  • Geer, J. G. (2012). The news media and the rise of negativity in presidential campaigns. PS: Political Science and Politics, 45(3), 422–7.

    Google Scholar 

  • Glassman, M. E., Straus, J. R., & Shogan, C. J. (2009). Social networking and constituent communication: Member use of twitter during a two-week period in the 111th Congres. Congressional Research Service.

  • Golbeck, J., Grimes, J., & Rogers, A. (2010). Twitter use by the U.S. congress. Journal of the American Society for Information Science and Technology, 61(8), 1612–1621.

    Google Scholar 

  • Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013). What makes a video go viral? An analysis of emotional contagion and internet memes. Computers in Human Behavior, 29(6), 2312–2319.

    Article  Google Scholar 

  • Hibbing, J. R., Smith, K. B., & Alford, J. R. (2014). Differences in negativity bias underlie variations in political ideology. Behavioral and Brain Sciences, 37(3), 297–307.

    Article  Google Scholar 

  • Kahn, K. F., & Kenney, P. J. (1999). The spectacle of U.S. Senate campaigns. Princeton University Press.

    Book  Google Scholar 

  • Kernell, S. (1977). Presidential popularity and negative voting: An alternative explanation of the midterm congressional decline of the president’s party. The American Political Science Review, 71(1), 44–66.

    Article  Google Scholar 

  • Klein, J. (1991). Negativity effects in impression formation: A test in the political arena. Personality and Social Psychology Bulletin, 17(4), 412–418.

    Article  Google Scholar 

  • Klein, J. (1996). Negativity in impressions of presidential candidates revisited: The 1992 election. Personality and Social Psychology Bulletin, 22(3), 288–295.

    Article  Google Scholar 

  • Koh, J., & O'Regan, D. (2017). How Russian facebook ads used race and religion to Inflame divisions. Washington Post, November 3, 2017.

  • Lassen, D. S., & Brown, A. R. (2011). Twitter: The electoral connection? Social Science Computer Review, 29(4), 419–436.

    Article  Google Scholar 

  • Lawless, J. L. (2012). New ways for members of Congress to send the same old messages? In R. L. Fox & J. M. Ramos (Eds.), iPolitics: Citizens, elections, and governing in the new media era. Cambridge University Press.

    Google Scholar 

  • Lee, J., Agrawal, M., & Rao, H. R. (2015). Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013. Information Systems Frontiers, 17(5), 997–1005.

    Article  Google Scholar 

  • Lewis J. B., Poole K., Rosenthal H., Boche A., Rudkin A., & Sonnet L. (2021). Voteview: Congressional Roll-Call Votes Database. https://voteview.com/

  • Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using stata (2nd ed.). STATA Press.

    Google Scholar 

  • Mutz, D. C. (2015). In-your-face politics: The consequences of uncivil media. Princeton University Press.

    Book  Google Scholar 

  • Poole, K. T., & Rosenthal, H. (1997). Congress: A political-economic history of roll call voting. Congressional Quarterly Press.

    Google Scholar 

  • Rimé, B. (2009). Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion Review, 1(1), 60–85.

    Article  Google Scholar 

  • Rossini, P., Hemsley, J., Tanupabrungsun, S., Zhang, F., & Stromer-Galley, J. (2018). Social media, opinion polls, and the use of persuasive messages during the 2016 US Election Primaries. Social Media + Society, 4(3), 1–11.

    Article  Google Scholar 

  • Rossini, P., Stromer-Galley, J., & Zhang, F. (2020). Exploring the relationship between campaign discourse on facebook and the public’s comments: A case study of incivility during the 2016 US Presidential Election. Political Studies, 2020, 1–19.

    Google Scholar 

  • Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320.

    Article  Google Scholar 

  • Schachter, S. (1959). The psychology of affiliation: Experimental studies of the sources of gregariousness. Stanford University Press.

    Google Scholar 

  • Settle, J. E. (2018). Frenemies: How social media polarizes America. Cambridge University Press.

    Book  Google Scholar 

  • Soroka, S. N. (2014). Negativity in democratic politics: Causes and consequences. Cambridge University Press.

    Book  Google Scholar 

  • Soroka, S. N., Fournier, P., Nir, L., & Hibbing, J. (2019). Psychophysiology in the study of political communication: An expository study of individual-level variation in negativity bias. Political Communication, 26(2), 288–302.

    Article  Google Scholar 

  • Soroka, S. N., & McAdams, S. (2015). News, politics, and negativity. Political Communication, 32(1), 1–22.

    Article  Google Scholar 

  • Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.

    Article  Google Scholar 

  • Straus, J. R., Glassman, M. E., Shogan, C. J., & Smelcer, S. (2013). Communicating in 140 characters or less: Congressional adoption of twitter in the 111th Congress. PS: Political Science & Politics, 46(1), 60–6.

    Google Scholar 

  • Straus, J. R., Williams, R. T., Shogan, C. J., & Glassman, M. E. (2016). Congressional social media communications: Evaluating senate twitter usage. Online Information Review, 40(5), 643–659.

    Article  Google Scholar 

  • Stromer-Galley, J. (2014). Presidential campaigning in the internet age. Oxford University Press.

    Book  Google Scholar 

  • Stromer-Galley, J., Zhang, F., Hemsley, J., & Tanupabrungsun, S. (2018). Tweeting the attack: Predicting gubernatorial candidate attack messaging and its spread. International Journal of Communication, 12, 3511–3532.

    Google Scholar 

  • Sydnor, E. (2018). Platforms for incivility: Examining perceptions across different media formats. Political Communication, 35(1), 97–116.

    Article  Google Scholar 

  • Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., & Hutchings, V. L. (2011). Election night’s alright for fighting: The role of emotions in political participation. The Journal of Politics, 73(1), 156–170.

    Article  Google Scholar 

  • Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

    Article  Google Scholar 

  • Wagner, K. M., Gainous, J., & Holman, M. R. (2017). I am woman, hear me tweet! gender differences in twitter use among congressional candidates. Journal of Women, Politics, and Policy, 38(4), 430–455.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Hudson Smith for his invaluable help at various stages of this project, Dylan Erikson and Riley Stotzky for their excellent research assistance, and Mac Avery his helpful feedback on drafts of this work.

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Authors and Affiliations

  1. Clemson University, 232 Brackett Hall, Clemson, SC, 29634, USA

    Jeffrey A. Fine

  2. Johns Hopkins University, Baltimore, USA

    Megan F. Hunt

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Correspondence to Jeffrey A. Fine.

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Fine, J.A., Hunt, M.F. Negativity and Elite Message Diffusion on Social Media. Polit Behav 45, 955–973 (2023). https://doi.org/10.1007/s11109-021-09740-8

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