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.
Our team has some other work that contributing to the techniques used in the demo system.
@article{ma-etal-2024-middag,
doi = {10.48550/ARXIV.2310.02529},
url = {https://arxiv.org/abs/2310.02529},
title={MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways},
author={Mingyu Derek Ma and Alexander K. Taylor and Nuan Wen and Yanchen Liu and Po-Nien Kung and Wenna Qin and Shicheng Wen and Azure Zhou and Diyi Yang and Xuezhe Ma and Nanyun Peng and Wei Wang},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
month=feb,
year={2024}
}