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While all the models significantly outperform the keyword-based baseline classifier, XGBoost using all features performs the best (F1\u2009=\u20090.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions. Findings support the generalizability of the best model, as the platform-specific results from Twitter and Wikipedia are comparable to their respective source papers. We make our code publicly available for application in real software systems as well as for further development by online hate researchers.<\/jats:p>","DOI":"10.1186\/s13673-019-0205-6","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T03:02:52Z","timestamp":1577934172000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":223,"title":["Developing an online hate classifier for multiple social media platforms"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3230-0561","authenticated-orcid":false,"given":"Joni","family":"Salminen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Hopf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shammur A.","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soon-gyo","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hind","family":"Almerekhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard J.","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,2]]},"reference":[{"key":"205_CR1","doi-asserted-by":"crossref","unstructured":"Castelle M. 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