{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:26:24Z","timestamp":1771025184133,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pontificia Universidad Javeriana","award":["21186"],"award-info":[{"award-number":["21186"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The growing popularity of social networking platforms worldwide has substantially increased the presence of offensive language on these platforms. To date, most of the systems developed to mitigate this challenge focus primarily on English content. However, this issue is a global concern, and therefore, other languages, such as Spanish, are involved. This article addresses the task of identifying hate speech, racism, and misogyny in Spanish within the Colombian context on social networks, and introduces a gold standard dataset specifically developed for this purpose. Indeed, the experiment compares the performance of TLM models from Deep Learning methods, such as BERT, Roberta, XLM, and BETO adjusted to the Colombian slang domain, then compares the best TLM model against a GPT, having a significant impact on achieving more accurate predictions in this task. Finally, this study provides a detailed understanding of the different components used in the system, including the architecture of the models and the selection of functions. The best results show that the BERT model achieves an accuracy of 83.6% for hate speech detection, while the GPT model achieves an accuracy of 90.8% for racism speech and 90.4% for misogyny detection.<\/jats:p>","DOI":"10.3390\/bdcc8090113","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T03:22:46Z","timestamp":1725592966000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection of Hate Speech, Racism and Misogyny in Digital Social Networks: Colombian Case Study"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8853-2455","authenticated-orcid":false,"given":"Luis Gabriel","family":"Moreno-Sandoval","sequence":"first","affiliation":[{"name":"Engineering Faculty, Pontificia Universidad Javeriana, Bogot\u00e1 110231, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2639-2474","authenticated-orcid":false,"given":"Alexandra","family":"Pomares-Quimbaya","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Pontificia Universidad Javeriana, Bogot\u00e1 110231, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5243-0238","authenticated-orcid":false,"given":"Sergio Andres","family":"Barbosa-Sierra","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Pontificia Universidad Javeriana, Bogot\u00e1 110231, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6267-5852","authenticated-orcid":false,"given":"Liliana Maria","family":"Pantoja-Rojas","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Universidad Distrital Francisco Jos\u00e9 de Caldas, Bogot\u00e1 111611, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,6]]},"reference":[{"key":"ref_1","unstructured":"Ash Turner (2023, November 15). 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