Abusive language detection in youtube comments leveraging replies as conversational context

Noman Ashraf; Arkaitz Zubiaga; Alexander Gelbukh

doi:10.7717/peerj-cs.742

Abusive language detection in youtube comments leveraging replies as conversational context

PeerJ Comput Sci. 2021 Oct 8:7:e742. doi: 10.7717/peerj-cs.742. eCollection 2021.

Authors

Noman Ashraf¹, Arkaitz Zubiaga², Alexander Gelbukh¹

Affiliations

¹ Instituto Politécnico Nacional, CIC, Mexico City, Mexico.
² Queen Mary University of London, London, United Kingdom.

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.

Keywords: Abusive language detection; Context aware abusive language detection; Corpus; Deep learning; Natural language processing; YouTube.

Grants and funding

The work was done with support from the Mexican Government through the grant A1-S-47854 of the CONACYT, Mexico and grants 20211784, 20211884, and 20211178 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico. The authors utilize the computing resources brought to them by the CONACYT through the Plataforma de Aprendizaje Profundo para Tecnologías del Lenguaje of the Laboratorio de Supercómputo of the INAOE, Mexico. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.