# Modeling Trolling in Social Media Conversations

arXiv: https://arxiv.org/abs/1612.05310 · HTML: https://mlsanity.com/paper/1612.05310/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [The Uli Dataset: An Exercise in Experience Led Annotation of oGBV](https://mlsanity.com/paper/2311.09086/) (2023)
- [A survey of textual cyber abuse detection using cutting-edge language models and large language models](https://mlsanity.com/paper/2501.05443/) (2025)
- [NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes](https://mlsanity.com/paper/2103.00466/) (2021)
- [TrollsWithOpinion: A Dataset for Predicting Domain-specific Opinion Manipulation in Troll Memes](https://mlsanity.com/paper/2109.03571/) (2021)
- [Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions](https://mlsanity.com/paper/2404.11752/) (2024)
- [Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages](https://mlsanity.com/paper/2401.03677/) (2024)

## Abstract (arXiv)

Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls' and the responders' perspectives, characterizing these two perspectives using four aspects, namely, the troll's intention and his intention disclosure, as well as the responder's interpretation of the troll's intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. Finally, we identify the difficult-to-classify cases in our corpus and suggest potential solutions for them.