This paper presents a two-step approach to finding and confronting dis/misinformation on social media.
This article describes a novel two-step approach of detecting and understanding dis/misinformation events in social media that occur during disasters and crisis events. To detect false news events, the authors designed a deep learning-based detection algorithm and then trained it with a transfer learning scheme so that the algorithm could decide whether a given group of rumor-related tweets is a dis/misinformation event. For understanding how dis/misinformation was diffused in social networks and identifying those who are responsible for creating and consuming false information, the researchers present DismisInfoVis, which consists of various visualizations, including a social network graph, a map, line charts, pie charts, and bar charts. By integrating these deep learning and multi-view visualization techniques, the authors could gain a deeper insight into dis/misinformation events in social media from multiple angles. This article describes in detail the implementation, training process, and performance evaluations of the detection algorithm and the design and utilization of DismisInfoVis for dis/misinformation data analyses. The authors hope that this study will contribute to improving the quality of information generated and shared on social media during critical times, eventually helping both the affected and the general public recover from the impacts of disasters and crisis events. (Published Abstract Provided)