MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
This addresses the challenge of tracking and predicting information spread for social media analysts and researchers, though it is incremental as it builds on existing visualization and forecasting methods.
The researchers tackled the problem of understanding COVID-19 news propagation on social media by developing MIDDAG, an interactive system that visualizes information diffusion paths and provides insights like community susceptibility and popular opinions, enabling forecasting and higher-level analysis of information flow.
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights, including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information. Besides discovering information flow patterns among users, we construct communities among users and develop the propagation forecasting capability, enabling tracing and understanding of how information is disseminated at a higher level.