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#FluxFlow: Visual analysis of anomalous information spreading on social media

Zhao, J and Cao, N and Wen, Z and Song, Y and Lin, YR and Collins, C (2014) #FluxFlow: Visual analysis of anomalous information spreading on social media. In: UNSPECIFIED.

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We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Zhao, J
Cao, N
Wen, Z
Song, Y
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Collins, C
Date: 31 December 2014
Date Type: Publication
Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 20
Number: 12
Page Range: 1773 - 1782
DOI or Unique Handle: 10.1109/tvcg.2014.2346922
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1077-2626
Date Deposited: 23 Jun 2014 21:57
Last Modified: 02 Apr 2021 19:55


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