  Moderation of user-generated content in an online community is a challenge
that has great socio-economical ramifications. However, the costs incurred by
delegating this work 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
F-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 most of the performance
(82.65).
