Supporting comment moderators in identifying high quality online news comments


Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.

In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, Best Paper Honorable Mention Award

The CommentIQ UI showing toggleable visualizations such as scatterplot, map, and timeline (left) that enable overview and filtering of comments, as well as an adjustable ranking based on various weighted quality criteria (right).