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MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
October 04, 2023 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Mingyu Derek Ma, Alexander K. Taylor, Nuan Wen, Yanchen Liu, Po-Nien Kung, Wenna Qin, Shicheng Wen, Azure Zhou, Diyi Yang, Xuezhe Ma, Nanyun Peng, Wei Wang
arXiv ID
2310.02529
Category
cs.SI: Social & Info Networks
Cross-listed
cs.AI,
cs.HC
Citations
3
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
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.
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