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ABSTRACT
Emotional expressions have been widely used in online news. Existing research on the perception of online news has primarily focused on the effect of contextual cues on readers’ reasoning and deliberation behavior; the role of discrete emotions such as anger and sadness, however, has been overlooked. This paper addresses this research gap by investigating the influence of angry and sad expressions in online news on readers’ perception of the news. Drawing on the emotions as social information (EASI) theory and the appraisal-tendency framework (ATF), we find that expressions of anger in online news decrease its believability. However, sad expressions do not trigger the same effect. A further test reveals that the effect of angry expressions can be explained by the readers’ perception of the author’s cognitive effort: readers perceive that expressions of anger in the headlines denote a lack of cognitive effort of the author in writing the news, which subsequently lowers the believability of the news. We also show that news believability has downstream implications and can impact various social media behaviors including reading, liking, commenting, and sharing. This research extends current knowledge of the cognitive appraisals and interpersonal effects of discrete emotions (i.e., anger, sadness) on online news. The results also offer practical implications for social media platforms, news aggregators, and regulators that need to manage digital content and control the spread of fake news.
Acknowledgements
We are extremely grateful to the guest editors and the anonymous reviewers for their invaluable comments and suggestions throughout the review process. We also thank all participants of the experiments.
Supplementary material
Supplemental data for this article can be accessed on the publisher’s website.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 The term perceived author’s cognitive effort refers to the level of cognitive effort of the author as perceived by readers..
Additional information
Notes on contributors
Bingjie Deng
Bingjie Deng ([email protected]) is a doctoral student in information systems in the Faculty of Business and Economics at the University of Hong Kong. She received her Master’s degree in Economics from that university. She is interested in consumer information processing, human-computer interaction, social media, and business analytics.
Michael Chau
Michael Chau ([email protected]) is an associate professor in the Faculty of Business and Economics at the University of Hong Kong. He received his Ph.D. in Management Information Systems from the University of Arizona. Dr. Chau’s research focuses on the cross-disciplinary intersection of information systems, computer science, business analytics, and information science, with an emphasis on the applications of data, text, and web mining in various business, education, and social domains. He has received multiple awards for his research.