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

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.

Gleaning Wisdom from the Past: Early Detection of Emerging Rumors in Social Media

“…Through representing data via intuitive visualization, experts can observe and understand how rumors spread from node to node, so that they are enabled to supervise the learning procedure of rumor classifiers with their domain knowledge and expertise [5,32].…”

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

“…There are also some existing works about human abnormal behavior analysis supported by visualization or visual analytics. In terms of human online communication behaviors, Caonan et al [17,18] designed several novel visualizations to help users understand the analysis results of anomaly recognition algorithm based on machine learning and analyze the behavior patterns of anomalous persons who are potential threats to society. For increased situational awareness and decision making in emergency response, Yuri [19] and Kim [20] visualized people's laws of daily activities in public areas and their movement in emergencies from the data captured by cameras and motion sensors in buildings.…”

Geoparsing and Geosemantics for Social Media

“…These visualizations allow semi-automated interactive analysis with questions such as who are the individuals who have the biggest impact on a specific rumours explored. Other work [Zhao 2014] has examined overlaying social network interconnections to temporal graphs of rumour retweets, revealing active users in both graphs during propagation periods as a rumour goes viral. Our work on geosemantic features and spatio-temporal visualization through map-based visualizations is quite complimentary to these other approaches, and could easily work in conjunction to them.…”