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

dc.contributor.author Caron, Matthew
dc.contributor.author Bäumer, Frederik S.
dc.contributor.author Müller, Oliver
dc.date.accessioned 2021-12-24T17:23:15Z
dc.date.available 2021-12-24T17:23:15Z
dc.date.issued 2022-01-04
dc.description.abstract Our 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.098
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79428
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Text Analytics
dc.subject hate speech detection
dc.subject machine learning
dc.subject natural language processing
dc.subject text analytics
dc.subject toxic language identification
dc.title Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models
dc.type.dcmi text
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