Efficient multi-account detection on UGC sites
Xucheng Luo, Fan Zhou, Mengjuan Liu, Yajun Liu, Chunjing Xiao · 2016
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.