NewsComp: Facilitating Diverse News Reading through Comparative Annotation

To support efficient, balanced news consumption, merging articles from diverse sources into one, potentially through crowdsourcing, could alleviate some hurdles. However, the merging process could also impact annotators' attitudes towards the content. To test this theory, we propose comparative news annotation, i.e., annotating similarities and differences between a pair of articles. By developing and deploying NewsComp -- a prototype system -- we conducted a between-subjects experiment(N=109) to examine how users' annotations compare to experts', and how comparative annotation affects users' perceptions of article credibility and quality. We found that comparative annotation can marginally impact users' credibility perceptions in certain cases. While users' annotations were not on par with experts', they showed greater precision in finding similarities than in identifying disparate important statements. The comparison process led users to notice differences in information placement/depth, degree of factuality/opinion, and empathetic/inflammatory language use. We discuss implications for the design of future comparative annotation tasks.
Comment: 2023 ACM CHI Conference on Human Factors in Computing Systems, 17 pages