Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models

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dc.contributor.authorCaron, Matthew
dc.contributor.authorBäumer, Frederik S.
dc.contributor.authorMüller, Oliver
dc.date.accessioned2021-12-24T17:23:15Z
dc.date.available2021-12-24T17:23:15Z
dc.date.issued2022-01-04
dc.description.abstractOur world is more connected than ever before. Sadly, however, this highly connected world has made it easier to bully, insult, and propagate hate speech on the cyberspace. Even though researchers and companies alike have started investigating this real-world problem, the question remains as to why users are increasingly being exposed to hate and discrimination online. In fact, the noticeable and persistent increase in harmful language on social media platforms indicates that the situation is, actually, only getting worse. Hence, in this work, we show that contemporary ML methods can help tackle this challenge in an accurate and cost-effective manner. Our experiments demonstrate that a universal approach combining transfer learning methods and state-of-the-art Transformer architectures can trigger the efficient development of toxic language detection models. Consequently, with this universal approach, we provide platform providers with a simplistic approach capable of enabling the automated moderation of user-generated content, and as a result, hope to contribute to making the web a safer place.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.098
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79428
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectText Analytics
dc.subjecthate speech detection
dc.subjectmachine learning
dc.subjectnatural language processing
dc.subjecttext analytics
dc.subjecttoxic language identification
dc.titleTowards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models
dc.type.dcmitext

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