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Analysis of Online Toxicity Detection Using Machine Learning Approaches

arXiv 2108.01062 ↗First posted 23 Apr 2021cs.CL cs.CY cs.LGcited by 0

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Abstract (arXiv)

Social media and the internet have become an integral part of how people spread and consume information. Over a period of time, social media evolved dramatically, and almost half of the population is using social media to express their views and opinions. Online hate speech is one of the drawbacks of social media nowadays, which needs to be controlled. In this paper, we will understand how hate speech originated and what are the consequences of it; Trends of machine-learning algorithms to solve an online hate speech problem. This study contributes by providing a systematic approach to help researchers to identify a new research direction and elucidating the shortcomings of the studies and model, as well as providing future directions to advance the field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 27 references that are in the map. First posted on arXiv 23 Apr 2021.

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3 of 27 references.

References · 3

  1. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  2. 2019A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Mediacited by 96
  3. 2013Tweet, but Verify: Epistemic Study of Information Verification on Twittercited by 3

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