Conversational Networks for Automatic Online Moderation

• Published in: IEEE transactions on computational social systems Vol. 6; no. 1; pp. 38 - 55
• Main Authors: Papegnies, Etienne, Labatut, Vincent, Dufour, Richard, Linares, Georges
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-02-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification algorithms; Companies; Computation and Language; Computer & video games; Computer Science; Computers and Society; Computing time; Document and Text Processing; Feature extraction; Games; Information Retrieval; Internet; Network theory (graphs); Parameter identification; Social and Information Networks; Social computing; Task analysis; Text analysis; Training; User generated content; Social computing; Information retrieval; Text analysis; Classification algorithms; Network theory (graphs)
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2018.2887240

Moderation of user-generated content in an online community is a challenge that has great socio-economic ramifications. However, the costs incurred by delegating this paper to human agents are high. For this reason, an automatic system able to detect abuse in user-generated content is of great interest. There are a number of ways to tackle this problem, but the most commonly seen in practice are word filtering or regular expression matching. The main limitations are their vulnerability to intentional obfuscation on the part of the users, and their context-insensitive nature. Moreover, they are language dependent and may require appropriate corpora for training. In this paper, we propose a system for automatic abuse detection that completely disregards message content. We first extract a conversational network from raw chat logs and characterize it through topological measures. We then use these as features to train a classifier on our abuse detection task. We thoroughly assess our system on a dataset of user comments originating from a French massively multiplayer online game. We identify the most appropriate network extraction parameters and discuss the discriminative power of our features, relatively to their topological and temporal nature. Our method reaches an <inline-formula> <tex-math notation="LaTeX">F </tex-math></inline-formula>-measure of 83.89 when using the full feature set, improving on existing approaches. With a selection of the most discriminative features, we dramatically cut computing time while retaining the most of the performance (82.65).