The role of affective and cognitive involvement in the mitigating effects of AI source cues on hostile media bias
Abstract
With an experimental design, this study examined the effect of source cues (Human vs. AI) on hostile media bias through heuristic machine evaluation of machine and human social media profiles. This study also explored the effects of affective and cognitive involvement as moderators along with media source-self ideological incongruity (source incongruity). A 2 (human vs. AI) x 3 (CNN vs. USA Today vs. Fox News) experimental study was conducted (n = 434). Participants exhibited less hostile media bias when presented with a news story with AI source cues through heuristic machine evaluation. The mitigating effect was stronger for those viewing news from an incongruent news source. Such moderated mediated effect was further moderated by two types of involvement (i.e., affective and cognitive). Implications for future research surrounding the two types of involvement, source incongruity, machine heuristic evaluations, and hostile media bias are discussed in light of our findings.
Matthew J. A. Craig
Assistant Professor of Computer-Mediated Communication
Matthew Craig is an Assistant Professor of Computer-Mediated Communication in the School of Communication, Journalism, and Media at Central Michigan University (CMU) and lab faculty with the Communication and Social Robotics Labs (COMBOTLABS). Before CMU, Matthew was the inaugural College of Communication and Information Postdoctoral Research Associate in the Information Integrity Institute at Tennessee’s flagship university, the University of Tennessee, Knoxville (Dr. Catherine Luther, Faculty Mentor). Matthew’s research interests are in human-machine communication and new media, focusing on the intersections of human-machine communication, privacy management, and society.