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

Cao, N and Shi, C and Lin, S and Lu, J and Lin, YR and Lin, CY (2016) TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems. IEEE Transactions on Visualization and Computer Graphics, 22 (1). 280 - 289. ISSN 1077-2626

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Abstract

© 1995-2012 IEEE. 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Cao, N
Shi, C
Lin, S
Lu, J
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Lin, CY
Date: 31 January 2016
Date Type: Publication
Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 22
Number: 1
Page Range: 280 - 289
DOI or Unique Handle: 10.1109/tvcg.2015.2467196
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1077-2626
PubMed ID: 26529707
Date Deposited: 28 Jun 2016 16:14
Last Modified: 14 Jun 2019 14:55
URI: http://d-scholarship.pitt.edu/id/eprint/28284

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