# Hunting for Troll Comments in News Community Forums

arXiv: https://arxiv.org/abs/1911.08113 · HTML: https://mlsanity.com/paper/1911.08113/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls](https://mlsanity.com/paper/2208.03295/) (2022)
- [ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls](https://mlsanity.com/paper/2208.00176/) (2022)
- [Exposing Paid Opinion Manipulation Trolls](https://mlsanity.com/paper/2109.13726/) (2021)
- [Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling](https://mlsanity.com/paper/2410.04164/) (2024)
- [SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering](https://mlsanity.com/paper/2109.15120/) (2021)
- [A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets](https://mlsanity.com/paper/2207.08230/) (2022)

## Abstract (arXiv)

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.