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A study towards contextual understanding of toxicity in online conversations

Madhyastha, P. ORCID: 0000-0002-4438-8161, Founta, A. & Specia, L. (2023). A study towards contextual understanding of toxicity in online conversations. Natural Language Engineering, 29(6), pp. 1538-1560. doi: 10.1017/s1351324923000414


Identifying and annotating toxic online content on social media platforms is an extremely challenging problem. Work that studies toxicity in online content has predominantly focused on comments as independent entities. However, comments on social media are inherently conversational, and therefore, understanding and judging the comments fundamentally requires access to the context in which they are made. We introduce a study and resulting annotated dataset where we devise a number of controlled experiments on the importance of context and other observable confounders – namely gender, age and political orientation – towards the perception of toxicity in online content. Our analysis clearly shows the significance of context and the effect of observable confounders on annotations. Namely, we observe that the ratio of toxic to non-toxic judgements can be very different for each control group, and a higher proportion of samples are judged toxic in the presence of contextual information.

Publication Type: Article
Additional Information: This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article. © The Author(s), 2023. Published by Cambridge University Press
Publisher Keywords: Natural language in multimodal and multimedia systems, Corpus annotation, Understanding toxic language
Subjects: P Language and Literature > P Philology. Linguistics
T Technology > T Technology (General)
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
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Available under License Creative Commons Attribution Non-commercial No Derivatives.

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