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

Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar
11/13/2019
cs.LGcs.CYcs.SI

Official code: mayasrikanth/TwitterStudiesCode (7 stars, last commit 2020-08-28).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

mayasrikanth/TwitterStudiesCodeOfficial100%
70C, RubyNov 12, 20196 years ago

Similar Papers

A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets

85% match2022

A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

84% match2023

Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts

84% match2023

From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

83% match2026

Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection

83% match2026

Session-based Cyberbullying Detection in Social Media: A Survey

83% match2022

Cite this paper

@article{liu2019finding,
  title  = {Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates},
  author = {Anqi Liu and Maya Srikanth and Nicholas Adams-Cohen and R. Michael Alvarez and Anima Anandkumar},
  year   = {2019},
  eprint = {1911.05332},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1911.05332}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1911.05332.svg)](https://codeofpaper.com/paper/1911.05332)

Discussion