ArticlePeerJ. Computer science2021
Abusive language detection in youtube comments leveraging replies as conversational context.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 44 citations in OpenAlex.
- ECAsT: a large dataset for conversational search and an evaluation of metric robustness.PeerJ. Computer science · 2023Article
- Multi-class sentiment analysis of urdu text using multilingual BERT.Scientific reports · 2022Article
- Multi-label emotion classification of Urdu tweets.PeerJ. Computer science · 2022Article
- Investigating toxicity changes of cross-community redditors from 2 billion posts and comments.PeerJ. Computer science · 2022Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.
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