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MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways

Authors :
Ma, Mingyu Derek
Taylor, Alexander K.
Wen, Nuan
Liu, Yanchen
Kung, Po-Nien
Qin, Wenna
Wen, Shicheng
Zhou, Azure
Yang, Diyi
Ma, Xuezhe
Peng, Nanyun
Wang, Wei
Publication Year :
2023

Abstract

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.<br />Comment: To appear at AAAI'24. System demo video and more info: info-pathways.github.io

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2310.02529
Document Type :
Working Paper