Previous research demonstrates that controversy in the content of news articles influences individuals’ attention, selection, and distribution of news. Our study examines whether controversy in social media comment fields can trigger similar effects. Using a mixed-methods approach combining eye tracking with surveys, we run an experiment in which participants (n=96) were exposed to 40 Facebook news posts from a Swedish newspaper. Under each post, we manipulated a pair of comments to be in either agreement or disagreement with one another. We find that disagreement increases users’ attention to comments and decreases their likelihood to share the post. We also find a significant difference between the effects of comment controversy on hard vs. soft news. Compared to soft news, controversy in hard news comments reduced users’ attention to the comment field, as well as their likelihood to read the Facebook post.
Anamaria Dutceac Segesten is Senior Lecturer in European Studies at Lund University. She holds a PhD in Political Science from the Univeristy of Maryland (USA) and has been a postdoctoral research fellow at the University of Copenhagen. Research-wise, Dr. Dutceac Segesten has written extensively on the topic of nationalism, collective identity and conflict. She is currently driving several projects investigating the role of social media in democratic politics. Her pedagogical experience covers European Union politics, International Relations, Comparative Politics and East and Central European politics. Dr. Dutceac Segesten is co-founder of the European Studies discipline at Lund University. She has taught at universities in the United States and Scandinavia, where she has supervised over 40 theses at the under and post-graduate levels. She has been twice nominated for university-wide pedagogical awards. Dr. Dutceac Segesten is an active member of the SamTech think-tank, founded at Lund University for the purpose of providing expert advice on how technology impacts society, and of the Artificial Intelligence/Machine Learning network, which brings together engineers, data scientists, humanists, lawyers, and social scientists to discuss the development of AI and its practical, social, and ethical consequences.