#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

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

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

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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