NewsComp: Facilitating Diverse News Reading through Comparative Annotation

TL;DR

This paper introduces NewsComp, a system for comparative news annotation that helps users identify similarities and differences between articles, influencing perceptions of credibility and highlighting annotation challenges.

Contribution

The paper presents a novel comparative annotation method and system, along with an empirical study on its effects on user perceptions and annotation quality.

Findings

Comparative annotation marginally affects credibility perceptions.

Users are better at identifying similarities than differences.

Comparison reveals insights into article content and tone.

Abstract

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.