Examining The CoVCues Dataset: Supporting COVID Infodemic Research Through A Novel User Assessment Study

Abstract

The public confidence and trust in online healthcare information have been greatly dented following the COVID-19 pandemic, which triggered a significant rise in online health misinformation. Existing literature shows that different datasets have been created to aid with detecting false information associated with this COVID infodemic. However, most of these datasets contain mostly unimodal data, which comprise primarily textual cues, and not visual cues, like images, infographics, and other graphic data components. Prior works point to the fact that there are only a handful of multimodal datasets that support COVID misinformation identification, and they lack an organized, processed and analyzed repository of visual cues. The novel CoVCues dataset, which represents a varied set of image artifacts, addresses this gap and advocates for the use of visual cues towards detecting online health misinformation. As part of validating the contents and utility of our CoVCues dataset, we have conducted a preliminary user assessment study, where different participants have been surveyed through a set of questionnaires to determine how effectively these dataset images contribute to the user perceived information reliability. These survey responses helped provide early insights into how different stakeholder groups interpret visual cues in the context of online health information and communication. The findings from this novel user assessment study offer valuable feedback for refining our CoVCues dataset and for supporting our claim that visual cues are underutilized but useful in combating the COVID infodemic. To our knowledge, this user assessment research study, as described in this paper, is the first of its kind work, involving COVID visual cues, that demonstrates the important role that our CoVCues dataset can potentially play in aiding COVID infodemic related future research work.