Assessment Committee

• Associate professor Jakob Linaa Jensen, Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University (chair)
• Senior Lecturer Joe Stubbersfield, School of Psychology and Social Sciences, University of Winchester, UK
• Professor Annie Waldherr, Institute for Journalism and Communication, University of Vienna, Austria

Supervisors

• Professor Anja Bechmann, Department of Media and Journalism Studies, Aarhus University
• Associate professor Ethan Weed, Department of Linguistics, Cognitive Science and Semiotics, Aarhus University

The dissertation will be available for reading in a digital version before the defence

following a statement from the borrower promising to delete the file afterwards. If you wish to read the dissertation, please contact Petra de Place Bak peba@cc.au.dk .

The defence is scheduled for three hours and is open to the public. All are welcome.

Abstract

This Ph.D. dissertation examines how content biases influence the engagement level of social media posts. Social media accentuates the need for users to filter and select information by massively increasing the availability of information. At the same time, social media platforms track user behavior, offering the opportunity to study how content biases and platform affordances influence engagement levels (e.g., comments, likes, and shares) in naturalistic settings. Building on existing work from experimental literature, this dissertation studies large-scale observational data using computational methods. Informed by cultural evolution theory, media and communication theory, and sociological perspectives, the dissertation focuses on negativity bias, positivity bias, emotion bias, threat bias, social information bias, group bias, and platform affordances. It draws on cases of health communication, misinformation, and meta‑science discourse on Facebook and Twitter in the Nordics. Overall, the dissertation finds that content biases increase engagement in predictable ways with key variations across domains and platforms. The dissertation brings the study of content biases into the social media age and advances our understanding of how content factors shape online information sharing.