Exposing Paid Opinion Manipulation Trolls
This work addresses the challenge of identifying organized, paid trolls in online forums, which is important for maintaining integrity in digital discourse, though it is incremental as it builds on existing classification methods with a novel data approach.
The paper tackles the problem of detecting paid opinion manipulation trolls on Web forums by addressing the lack of training data, proposing to use users labeled as trolls by multiple people as a proxy, and showing that a classifier trained on this proxy data achieves good performance in distinguishing paid trolls from non-trolls.
Recently, Web forums have been invaded by opinion manipulation trolls. Some trolls try to influence the other users driven by their own convictions, while in other cases they can be organized and paid, e.g., by a political party or a PR agency that gives them specific instructions what to write. Finding paid trolls automatically using machine learning is a hard task, as there is no enough training data to train a classifier; yet some test data is possible to obtain, as these trolls are sometimes caught and widely exposed. In this paper, we solve the training data problem by assuming that a user who is called a troll by several different people is likely to be such, and one who has never been called a troll is unlikely to be such. We compare the profiles of (i) paid trolls vs. (ii)"mentioned" trolls vs. (iii) non-trolls, and we further show that a classifier trained to distinguish (ii) from (iii) does quite well also at telling apart (i) from (iii).