Hunting for Troll Comments in News Community Forums
This work addresses the issue of identifying trolls who manipulate public opinion, particularly in Bulgaria and Eastern Europe, but it is incremental as it applies existing classification methods to new data.
The paper tackled the problem of detecting opinion manipulation trolls in news community forums, focusing on paid trolls and mentioned trolls, and achieved classification accuracies of 81-82% for distinguishing these trolls from non-trolls.
There are different definitions of what a troll is. Certainly, a troll can be somebody who teases people to make them angry, or somebody who offends people, or somebody who wants to dominate any single discussion, or somebody who tries to manipulate people's opinion (sometimes for money), etc. The last definition is the one that dominates the public discourse in Bulgaria and Eastern Europe, and this is our focus in this paper. In our work, we examine two types of opinion manipulation trolls: paid trolls that have been revealed from leaked reputation management contracts and mentioned trolls that have been called such by several different people. We show that these definitions are sensible: we build two classifiers that can distinguish a post by such a paid troll from one by a non-troll with 81-82% accuracy; the same classifier achieves 81-82% accuracy on so called mentioned troll vs. non-troll posts.