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

TLDR

Anomalous user behaviors in online communication systems pose societal threats, yet automated detection struggles with limited ground truth, requiring human judgment and lacking efficient interpretability tools. TargetVue is introduced to detect anomalous users using unsupervised learning and to visualize their behaviors within rich contextual views. The system employs three ego‑centric glyphs to summarize communication activities, features, and social interactions, arranges them on a triangle grid to reveal user similarities, and validates the design through a Twitter bot challenge, an email case study, and expert interviews. Evaluation demonstrates that TargetVue improves the detection of users with anomalous communication behaviors.

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.