What type of emotional language spreads further in political discourses on social media? Previous research has focused on situations that primarily elicited negative emotions, showing that negative language tended to spread further. The current project extends existing knowledge by examining the spread of emotional language in response to both predominantly positive and negative political situations. In Study 1, we examined the spread of emotional language in tweets related to the winning and losing parties in the 2016 US elections, finding that increased negativity (but not positivity) predicted content sharing in both situations. In Study 2, we compared the spread of emotional language in two separate situations: the celebration of the US Supreme Court approval of same-sex marriage (positive) and the Ferguson unrest (negative), finding again that negativity spread further. These results shed light on the nature of political discourse and engagement.

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What type of emotional language spreads further in political discourses on social media? Previous research has focused on situations that primarily elicited negative emotions, showing that negative language tended to spread further. The current project addressed the gap introduced when looking only at negative situations by comparing the spread of emotional language in response to both predominantly positive and negative political situations. In Study 1, we examined the spread of emotional language among tweets related to the winning and losing parties in the 2016 US elections, finding that increased negativity (but not positivity) predicted content sharing in both situations. In Study 2, we compared the spread of emotional language in two separate situations: the celebration of the US Supreme Court approval of same-sex marriage (positive), and the Ferguson Unrest (negative), finding again that negativity spread further. These results shed light on the nature of political discourse and engagement.

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Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with the content of tweets. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media – public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers (and strength of ties), and proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed to mitigate overexposure to negative content.