Offensive Language Detection on Twitter
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Abstract (arXiv)
Detection of offensive language in social media is one of the key challenges for social media. Researchers have proposed many advanced methods to accomplish this task. In this report, we try to use the learnings from their approach and incorporate our ideas to improve upon them. We have successfully achieved an accuracy of 74% in classifying offensive tweets. We also list upcoming challenges in the abusive content detection in the social media world.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 28 Sep 2022.
Builds on
4 of 15 references.
References · 4
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
- 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 195
- 2013NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweetscited by 33
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