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Exemplars, i.e. the description of the beliefs and experiences of particular individuals are a central part of the news coverage of political polarization in traditional news media and studies argue that exposure to especially extreme partisan exemplars can increase affective polarization and outparty animus. As such the use of partisan exemplars in traditional news media is often emphasized as carrying central responsibility for affective polarization. This study shifts the focus to the mass level and considers the active role of citizens in engaging with and disseminating partisan exemplars on social media. We extend psychological research on cognitive biases for vivid social information holding that people exhibit an interpersonal transmission bias in favour of vivid exemplars and psychological research emphasizing a bias for attending to extreme and deviant information. Integrating these lines of research we argue that extreme partisan exemplars and uncivil partisan exemplars, respectively, will trigger more engagement in the form of emotional reactions, comments and shares on social media than moderate and civil partisan exemplars in news coverage on social media.
We test the predictions using a unique cross-national research combining survey experiments maximizing causal identification with analyses of real-world Facebook posts and tweets from news outlets in the United States and the United Kingdom and associated engagement metrics. We use machine learning for automated content analysis to code partisan exemplars and their extremity and incivility in the tweets and Facebook post. We conclude with a final experimental study investigating the effect of exposure to extreme and uncivil partisan exemplars in news reporting on social media on affective polarization, partisan stereotyping, and outgroup hostility in the United States and the United Kingdom. The results have implications for understanding how the news media supply of exemplar and mass level demand mechanisms for these narratives on social media shape affective polarization, partisan stereotyping, and outgroup animosity.