Detecting Harmful Content on Online Platforms: What Platforms Need vs. Where Research Efforts Go
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Accepted author manuscript, 6.66 MB, PDF document
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms, including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self-harm, and many others. Online platforms seek to moderate such content to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Researchers have developed different methods for automatically detecting harmful content, often focusing on specific sub-problems or on narrow communities, as what is considered harmful often depends on the platform and on the context. We argue that there is currently a dichotomy between what types of harmful content online platforms seek to curb, and what research efforts there are to automatically detect such content. We thus survey existing methods as well as content moderation policies by online platforms in this light and suggest directions for future work.
Original language | English |
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Article number | 72 |
Journal | ACM Computing Surveys |
Volume | 56 |
Issue number | 3 |
Pages (from-to) | 1-17 |
ISSN | 0360-0300 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- Additional Key Words and PhrasesOnline harms, bullying and harassment, content moderation, graphic content, hate speech, misinformation, offensive language, self-harm, sexual abuse, spam, violence
Research areas
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