# Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates

arXiv: https://arxiv.org/abs/1911.05332 · HTML: https://mlsanity.com/paper/1911.05332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions](https://mlsanity.com/paper/2211.11798/) (2022)
- [Machine-Learning media bias](https://mlsanity.com/paper/2109.00024/) (2021)
- [Dynamic Social Media Monitoring for Fast-Evolving Online Discussions](https://mlsanity.com/paper/2102.12596/) (2021)
- [Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion](https://mlsanity.com/paper/2208.13318/) (2022)

## Abstract (arXiv)

Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changing topics for more effective data collection and representing word semantics in different domains. We demonstrate with preliminary results that using the GloVe (Global Vectors for Word Representation) model facilitates the discovery of new and relevant keywords to use for data collection and trolling detection. Our paper concludes with a discussion of a research agenda to further develop and test word embedding models for identification of social media harassment and trolling.