TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems

Abstract

Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.

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

“…Though fact-checking sites may cover only a small portion of rumors in social media, the identified rumors offers us valuable resources to evaluate rumor detection algorithms. In this work, we choose Snopes 4 to obtain ground truth, which is the top rumor reference site according to Alexa 5 . In order to obtain non-rumor posts pertaining to the same topic, we extract keywords in regular expressions as queries to retrieve posts.…”

“…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.…”

Analysis of various Characteristics of Online User Behavior Models

“…TargetVue introduces 3 new egocentric glyphs to visually summarize a user's behaviors. These glyphs effectively represent the user's communication activities, features and social interactions [2].…”