Developing an online hate classifier for multiple social media platforms

Joni Salminen, Maximilian Hopf, Shammur A. Chowdhury, Soon gyo Jung, Hind Almerekhi, Bernard J. Jansen

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


The proliferation of social media enables people to express their opinions widely online. However, at the same time, this has resulted in the emergence of conflict and hate, making online environments uninviting for users. Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. To address this research gap, we collect a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labeled as non-hateful and the remaining 20% labeled as hateful. We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). While all the models significantly outperform the keyword-based baseline classifier, XGBoost using all features performs the best (F1 = 0.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions. Findings support the generalizability of the best model, as the platform-specific results from Twitter and Wikipedia are comparable to their respective source papers. We make our code publicly available for application in real software systems as well as for further development by online hate researchers.

Original languageEnglish (US)
Article number1
JournalHuman-centric Computing and Information Sciences
Issue number1
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science


Dive into the research topics of 'Developing an online hate classifier for multiple social media platforms'. Together they form a unique fingerprint.

Cite this