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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.

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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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