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Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

journal contribution
submitted on 2024-07-10, 11:07 and posted on 2024-07-10, 11:09 authored by Joni Salminen, Sercan Sengün, Juan Corporan, Soon-gyo Jung, Bernard J. Jansen

Hateful commenting, also known as ‘toxicity’, frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes “toxicified” when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic.

Other Information

Published in: PLOS ONE
See article on publisher's website:


Open Access funding provided by the Qatar National Library.



  • English


Public Library of Science (PLoS)

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

Related Datasets

Joni Salminen. (2020). website data collection script). Dropbox.