TL;DR
RumorLens is an interactive visual analytics system designed to help social media platform administrators efficiently analyze, validate, and understand the patterns of rumor spreading using NLP and innovative visualization techniques.
Contribution
The paper introduces RumorLens, a novel system combining NLP and advanced visualizations for rumor analysis, developed through close collaboration with social media administrators.
Findings
Effective visualization of rumor spatial and temporal patterns.
Enhanced understanding of rumor similarities and spreading dynamics.
Successful case study validation with real-world Sina Weibo data.
Abstract
With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
