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

Authors

  • Mingyu Derek Ma UCLA
  • Alexander K. Taylor UCLA
  • Nuan Wen University of Southern California
  • Yanchen Liu Stanford University Harvard University
  • Po-Nien Kung UCLA
  • Wenna Qin Stanford University
  • Shicheng Wen University of Southern California
  • Azure Zhou Stanford University
  • Diyi Yang Stanford University
  • Xuezhe Ma University of Southern California
  • Nanyun Peng UCLA
  • Wei Wang UCLA

DOI:

https://doi.org/10.1609/aaai.v38i21.30573

Keywords:

Artificial Intelligence, Natural language processing and speech recognition, Systems that integrate different AI technologies

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. A demo video and more are available at https://info-pathways.github.io.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Ma, M. D., Taylor, A. K., Wen, N., Liu, Y., Kung, P.-N., Qin, W., Wen, S., Zhou, A., Yang, D., Ma, X., Peng, N., & Wang, W. (2024). MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23811-23813. https://doi.org/10.1609/aaai.v38i21.30573

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Demonstration Track