#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Contributors

Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins

Abstract

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.

Publications

  • J. Zhao, N. Cao, Z. Wen, Y. Song, Y. Lin, and C. Collins, “#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media,” IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Visual Analytics Science and Technology (VAST)), vol. 20, pp. 1773-1782, 2014. Honorable Mention Award.
    [Bibtex] [PDF] [DOI]
    @Article{zha2014a,
      author = {Jian Zhao and Nan Cao and Zhen Wen and Yale Song and Yu-Ru Lin and Christopher Collins},
      title = {#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media},
      journal = {IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Visual Analytics Science and Technology (VAST))},
      year = 2014,
      volume = 20,
      issue = 12,
      month = dec,
      pages = {1773 - 1782},
      note = {Honorable Mention Award},
      doi = {10.1109/TVCG.2014.2346922}
    }

Video

 Acknowledgements

Collaboration between IBM Research, University of Toronto, MIT, University of Pittsburgh, and UOIT.

Research

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| © Copyright vialab | Dr. Christopher Collins, Canada Research Chair in Linguistic Information Visualization |