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Thus, automatic detection of hateful and offensive content on these platforms is a crucial challenge that would strongly contribute to an equal and sustainable society when overcome. One significant difficulty in meeting this challenge is collecting sufficient labeled data. In our work, we examine how various resources can be leveraged to circumvent this difficulty. We carry out extensive experiments to exploit various data sources using different machine learning models, including state-of-the-art transformers. We have found that using our proposed methods, one can attain state-of-the-art performance detecting hate speech on Twitter (outperforming the winner of both the HASOC 2019 and HASOC 2020 competitions). It is observed that in general, adding more data improves the performance or does not decrease it. Even when using good language models and knowledge transfer mechanisms, the best results were attained using data from one or two additional data sets.<\/jats:p>","DOI":"10.3233\/aic-210138","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T11:17:41Z","timestamp":1651835861000},"page":"87-109","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging external resources for offensive content detection in social media"],"prefix":"10.1177","volume":"35","author":[{"given":"Gy\u00f6rgy","family":"Kov\u00e1cs","sequence":"first","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Norrbotten, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Alonso","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Norrbotten, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajkumar","family":"Saini","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Norrbotten, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus","family":"Liwicki","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Norrbotten, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-210138_ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00387-6","article-title":"A survey on data-efficient algorithms in big data era","volume":"8","author":"Adadi","year":"2021","journal-title":"Journal of Big Data"},{"key":"10.3233\/AIC-210138_ref2","doi-asserted-by":"publisher","first-page":"203","DOI":"10.20901\/pm.55.4.08","article-title":"The legal regulation of hate speech: The international and European frameworks","volume":"55","author":"Alkiviadou","year":"2018","journal-title":"Politi\u010dka misao"},{"issue":"1","key":"10.3233\/AIC-210138_ref3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/13600834.2018.1494417","article-title":"Hate speech on social media networks: Towards a regulatory framework?","volume":"28","author":"Alkiviadou","year":"2019","journal-title":"Information & Communications Technology Law"},{"issue":"2","key":"10.3233\/AIC-210138_ref4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social media and fake news in the 2016 election","volume":"31","author":"Allcott","year":"2017","journal-title":"Journal of Economic Perspectives"},{"key":"10.3233\/AIC-210138_ref5","unstructured":"P. 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