---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matthew
      foaf_name: Caron, Matthew
      foaf_surname: Caron
      foaf_workInfoHomepage: http://www.librecat.org/personId=60721
  - foaf_Person:
      foaf_givenName: Frederik S.
      foaf_name: Bäumer, Frederik S.
      foaf_surname: Bäumer
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Müller, Oliver
      foaf_surname: Müller
      foaf_workInfoHomepage: http://www.librecat.org/personId=72849
  dct_date: 2022^xs_gYear
  dct_language: eng
  dct_title: 'Towards Automated Moderation: Enabling Toxic Language Detection with
    Transfer Learning and Attention-Based Models@'
...