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2016 IEEE Symposium on Computers and Communication (ISCC) (2016)
Messina, Italy
June 27, 2016 to June 30, 2016
ISBN: 978-1-5090-0680-9
pp: 450-455
Xucheng Luo , School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu China
Fan Zhou , School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu China
Mengjuan Liu , School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu China
Yajun Liu , School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu China
Chunjing Xiao , School of Computer and Information Engineering, Henan University, Kaifeng China
ABSTRACT
This work presents a novel writing style-based approach to detect multi-account users on User-Generated Content (UGC) sites. Unlike existing works which emphasize feasibility and privacy leakage, we focus on precise writing style-based multi-account detection. Specifically, we leverage a one-class classification-based approach to detect multi-account behaviors, in which a mutual similarity measurement is defined to increase detection precision. In addition to traditional features used in writing style detection, we also extract bigrams, trigrams, part-of-speech, and grammatical relations. We evaluate our methodology based on datasets crawled from 3 popular OSNs (i.e., Twitter, Facebook, and Google+). Experimental results demonstrate that compared with the most recent achievements, our method not only improves the average detection precision to almost 90%, but also increases both recall and F-measure to 90% and even better.
INDEX TERMS
Writing, Feature extraction, Correlation, User-generated content, Privacy, Data mining, Computers
CITATION

Xucheng Luo, Fan Zhou, Mengjuan Liu, Yajun Liu and Chunjing Xiao, "Efficient multi-account detection on UGC sites," 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 2016, pp. 450-455.
doi:10.1109/ISCC.2016.7543780
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