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Hunting for Troll Comments in News Community Forums

Mihaylov, Todor and Nakov, Preslav (2016):
Hunting for Troll Comments in News Community Forums.
In: Proceedings of the 54rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, [Online-Edition: http://www.aclweb.org/anthology/K15-1032],
[Conference or Workshop Item]

Abstract

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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Mihaylov, Todor and Nakov, Preslav
Title: Hunting for Troll Comments in News Community Forums
Language: German
Abstract:

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.

Title of Book: Proceedings of the 54rd Annual Meeting of the Association for Computational Linguistics
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: AIPHES_area_a2
Divisions: DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 30 Dec 2016 17:45
Official URL: http://www.aclweb.org/anthology/K15-1032
Identification Number: TUD-CS-2016-0158
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