User Experience Design for Automatic Credibility Assessment of News Content About COVID-19
This work addresses the need for user-friendly tools to combat misinformation about COVID-19, but it is incremental as it focuses on interface design rather than novel credibility assessment methods.
The study tackled the problem of designing user interfaces for automatic credibility assessment of COVID-19 news by evaluating usability through qualitative interviews and quantitative surveys, finding that rating scales, sub-criteria, and algorithm authorship are key predictors, with a conflict between transparency and conciseness in design.
The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of quality assurance. In that context, we check the credibility of news content using selected linguistic features. We present two empirical studies to evaluate the usability of graphical interfaces that offer such credibility assessment. In a moderated qualitative interview with six participants, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. A subsequent quantitative online survey with 50 participants reveals a conflict between transparency and conciseness in the interface design, as well as a perceived hierarchy of metadata: the authorship of a news text is more important than the authorship of the credibility algorithm used to assess the content quality. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment.