{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:18:55Z","timestamp":1792264735455,"version":"4.4.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content, such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation\u2014dubbed the Map of Hate\u2014in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.<\/jats:p>","DOI":"10.1007\/s42001-023-00224-9","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T07:01:56Z","timestamp":1697526116000},"page":"1081-1101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Transfer learning for hate speech detection in social media"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6992-7187","authenticated-orcid":false,"given":"Lanqin","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela","family":"Ferraro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Suominen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marian-Andrei","family":"Rizoiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"224_CR2","doi-asserted-by":"publisher","unstructured":"Agrawal, S., & Awekar, A. (2018). Deep learning for detecting cyberbullying across multiple social media platforms. In G. Pasi, B. Piwowarski, L. Azzopardi, A. Hanbury (Eds.) Advances in information retrieval. ECIR 2018. Lecture notes in computer science, vol 10772. Cham: Springer. https:\/\/doi.org\/10.1007\/978-3-319-76941-7_11","DOI":"10.1007\/978-3-319-76941-7_11"},{"key":"224_CR3","doi-asserted-by":"publisher","unstructured":"Awal, M. R., Cao, R., Lee, R. KW., & Mitrovi\u0107, S. (2021). AngryBERT: Joint learning target and emotion for hate speech detection. In: K. Karlapalem et al. (Eds.) Advances in knowledge discovery and data mining. PAKDD 2021. Lecture Notes in Computer Science, vol 12712. Cham: Springer. https:\/\/doi.org\/10.1007\/978-3-030-75762-5_55","DOI":"10.1007\/978-3-030-75762-5_55"},{"key":"224_CR4","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, M., & Varma, V. (2019). Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. In WWW\u201919 (pp. 49\u201359). ACM Press.","DOI":"10.1145\/3308558.3313504"},{"key":"224_CR5","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017). Deep learning for hate speech detection in tweets. In WWW \u201917 (pp. 759\u2013760). ACM Press.","DOI":"10.1145\/3041021.3054223"},{"key":"224_CR6","first-page":"99","volume-title":"The self-fulfilling effects of contemporary sexism: How the well-being and behavior of women is affected by the subtle discrimination they encounter","author":"M Barreto","year":"2009","unstructured":"Barreto, M., Ellemers, N., Cihangir, S., & Stroebe, K. (2009). The self-fulfilling effects of contemporary sexism: How the well-being and behavior of women is affected by the subtle discrimination they encounter (pp. 99\u2013124). American Psychological Association."},{"key":"224_CR1","unstructured":"Cambridge dictionary: hate speech."},{"key":"224_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.4000\/ijcol.659","volume":"6","author":"A Capozzi","year":"2020","unstructured":"Capozzi, A., Lai, M., Basile, V., Poletto, F., Sanguinetti, M., Bosco, C., Patti, V., Ruffo, G., Musto, C., Polignano, M., Semeraro, G., & Stranisci, M. (2020). \u201ccontro l\u2019odio\u2019\u2019: A platform for detecting, monitoring and visualizing hate speech against immigrants in Italian social media. Italian Journal of Computational Linguistics, 6, 77\u201397.","journal-title":"Italian Journal of Computational Linguistics"},{"key":"224_CR8","doi-asserted-by":"crossref","unstructured":"Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., & Vakali, A. (2017). Mean birds: Detecting aggression and bullying on Twitter. In WebSci \u201917 (pp. 13\u201322). ACM Press.","DOI":"10.1145\/3041021.3054211"},{"key":"224_CR9","doi-asserted-by":"crossref","unstructured":"Cheng, J., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2015). Antisocial behavior in online discussion communities. In ICWSM\u201915 (pp. 61\u201370). AAAI Press.","DOI":"10.1609\/icwsm.v9i1.14583"},{"key":"224_CR10","unstructured":"Daume III, H. (2007). Frustratingly easy domain adaptation. In Proceedings of the 45th annual meeting of the association of computational linguistics, Prague, Czech Republic (pp. 256\u2013263). Association for Computational Linguistics."},{"key":"224_CR11","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M. W., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In ICWSM\u201917. AAAI Press.","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"224_CR12","doi-asserted-by":"crossref","unstructured":"Fehn Unsv\u00e5g, E., & Gamb\u00e4ck, B. (2018). The effects of user features on Twitter hate speech detection. In Workshop on abusive language online (ALW2) (pp. 75\u201385). Association for Computational Linguistics.","DOI":"10.18653\/v1\/W18-5110"},{"issue":"4","key":"224_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3232676","volume":"51","author":"P Fortuna","year":"2018","unstructured":"Fortuna, P., & Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys, 51(4), 1\u201330.","journal-title":"ACM Computing Surveys"},{"key":"224_CR14","doi-asserted-by":"publisher","unstructured":"Founta, A. M., Chatzakou, D., Kourtellis, N., Blackburn, J., Vakali, A., & Leontiadis, I. (2019). A unified deep learning architecture for abuse detection. In Proceedings of the 10th ACM conference on web science (WebSci '19) (pp. 105\u2013114). New York, NY, USA: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3292522.3326028","DOI":"10.1145\/3292522.3326028"},{"issue":"4","key":"224_CR15","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1177\/01968599221124309","volume":"47","author":"M Hietanen","year":"2023","unstructured":"Hietanen, M., & Eddebo, J. (2023). Towards a definition of hate speech\u2014with a focus on online contexts. Journal of Communication Inquiry, 47(4), 440\u2013458. https:\/\/doi.org\/10.1177\/01968599221124309","journal-title":"Journal of Communication Inquiry"},{"key":"224_CR16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735\u201380.","journal-title":"Neural Computation"},{"issue":"8","key":"224_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"224_CR18","doi-asserted-by":"crossref","unstructured":"Hutto, C. J., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In ICWSM\u201914. AAAI Press.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"224_CR19","unstructured":"Jain, S., & Wallace, B. C. (2019). Attention is not explanation. In North American chapter of the Association for Computational Linguistics (pp. 3543\u20133556). Association for Computational Linguistics."},{"key":"224_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106458","volume":"210","author":"P Kapil","year":"2020","unstructured":"Kapil, P., & Ekbal, A. (2020). A deep neural network based multi-task learning approach to hate speech detection. Knowledge-Based Systems, 210, 106458.","journal-title":"Knowledge-Based Systems"},{"key":"224_CR21","doi-asserted-by":"crossref","unstructured":"Karan, M., & \u0160najder, J. (2018). Cross-domain detection of abusive language online. In Proceedings of the 2nd workshop on abusive language online (ALW2) (pp. 132\u2013137). Brussels, Belgium: Association for Computational Linguistics.","DOI":"10.18653\/v1\/W18-5117"},{"key":"224_CR22","unstructured":"Kumar, R., Ojha, A.\u00a0K., Malmasi, S., & Zampieri, M. (2018). Benchmarking aggression identification in social media. In Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018), Santa Fe, New Mexico, USA (pp. 1\u201311). Association for Computational Linguistics."},{"key":"224_CR23","unstructured":"Lange, J., Mollas, I., & Tsoumakas, G. (2018). Hatebusters: A web application for actively reporting YouTube hate speech. In Proceedings of the 27th international joint conference on artificial intelligence (pp. 5796\u20135798). International Joint Conferences on Artificial Intelligence."},{"issue":"7553","key":"224_CR24","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.","journal-title":"Nature"},{"key":"224_CR25","doi-asserted-by":"crossref","unstructured":"Liu, H., Burnap, P., Alorainy, W., & Williams, M. L. (2019). Fuzzy multi-task learning for hate speech type identification. In The World Wide Web conference on\u2014WWW \u201919 (pp. 3006\u20133012). ACM Press.","DOI":"10.1145\/3308558.3313546"},{"issue":"Nov","key":"224_CR26","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. JMLR, 9(Nov), 2579\u20132605.","journal-title":"JMLR"},{"issue":"5","key":"224_CR27","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/j.1751-9004.2010.00263.x","volume":"4","author":"AC Merritt","year":"2010","unstructured":"Merritt, A. C., Effron, D. A., & Monin, B. (2010). Moral self-licensing: When being good frees us to be bad. Social and Personality Psychology Compass, 4(5), 344\u2013357.","journal-title":"Social and Personality Psychology Compass"},{"key":"224_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113725","volume":"161","author":"S Modha","year":"2020","unstructured":"Modha, S., Majumder, P., Mandl, T., & Mandalia, C. (2020). Detecting and visualizing hate speech in social media: A cyber watchdog for surveillance. Expert Systems with Applications, 161, 113725.","journal-title":"Expert Systems with Applications"},{"key":"224_CR29","doi-asserted-by":"crossref","unstructured":"Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020). A BERT-based transfer learning approach for hate speech detection in online social media. In Complex networks and their applications VIII (pp. 928\u2013940). Cham: Springer.","DOI":"10.1007\/978-3-030-36687-2_77"},{"key":"224_CR30","doi-asserted-by":"crossref","unstructured":"Pamungkas, E.\u00a0W., & Patti, V. (2019). Cross-domain and cross-lingual abusive language detection: A hybrid approach with deep learning and a multilingual lexicon. In Proceedings of the 57th annual meeting of the association for computational linguistics: Student research workshop, Florence, Italy (pp. 363\u2013370). Association for Computational Linguistics.","DOI":"10.18653\/v1\/P19-2051"},{"issue":"10","key":"224_CR31","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. Transactions on Knowledge and Data Engineering, 22(10), 1345\u20131359.","journal-title":"Transactions on Knowledge and Data Engineering"},{"key":"224_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0138511","volume":"10","author":"Y Paradies","year":"2015","unstructured":"Paradies, Y., Ben, J., Denson, N., Elias, A., Priest, N., Pieterse, A., Gupta, A., Kelaher, M., & Gee, G. (2015). Racism as a determinant of health: A systematic review and meta-analysis. PLoS One, 10,  1-45.","journal-title":"PLoS One"},{"key":"224_CR33","doi-asserted-by":"crossref","unstructured":"Park, J. H., & Fung, P. (2017). One-step and two-step classification for abusive language detection on Twitter. In Workshop on abusive language online (pp. 41\u201345). Association for Computational Linguistics.","DOI":"10.18653\/v1\/W17-3006"},{"key":"224_CR34","unstructured":"Paszke, A., Gross, S., & Chintala, S., et al. (2017). Automatic differentiation in pytorch. In NeurIPS\u201917."},{"key":"224_CR35","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In EMNLP\u201914 (pp. 1532\u20131543). Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1162"},{"key":"224_CR36","doi-asserted-by":"crossref","unstructured":"Pereira-Kohatsu, J.\u00a0C., Quijano-S\u00e1nchez, L., Liberatore, F., & Camacho-Collados, M. (2019). Detecting and monitoring hate speech in twitter. Sensors, 19(21).","DOI":"10.3390\/s19214654"},{"key":"224_CR37","doi-asserted-by":"crossref","unstructured":"Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of NAACL (pp. 2227\u20132237). Association for Computational Linguistics.","DOI":"10.18653\/v1\/N18-1202"},{"issue":"12","key":"224_CR38","doi-asserted-by":"publisher","first-page":"4730","DOI":"10.1007\/s10489-018-1242-y","volume":"48","author":"GK Pitsilis","year":"2018","unstructured":"Pitsilis, G. K., Ramampiaro, H., & Langseth, H. (2018). Effective hate-speech detection in Twitter data using recurrent neural networks. Applied Intelligence, 48(12), 4730\u20134742.","journal-title":"Applied Intelligence"},{"key":"224_CR39","doi-asserted-by":"publisher","unstructured":"Rajamanickam, S., Mishra, P., Yannakoudakis, H., & Shutova, E. (2020). Joint modelling of emotion and abusive language detection. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 4270\u20134279). Association for Computational Linguistics (ACL). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.394","DOI":"10.18653\/v1\/2020.acl-main.394"},{"key":"224_CR40","unstructured":"Reuters (2018). Why Facebook is losing the war on hate speech in Myanmar. https:\/\/www.reuters.com\/investigates\/special-report\/myanmar-facebook-hate\/. Accessed 16 May 2019."},{"key":"224_CR41","unstructured":"Rumelhart, D. E. & McClelland, J. L. (1987). Learning internal representations by Error Propagation. In Parallel distributed processing: Explorations in the microstructure of cognition: Foundations (pp. 318\u2013362), MIT Press."},{"key":"224_CR42","volume-title":"Advances in neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. U., & Polosukhin, I. (2017). Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems.  (Vol. 30). Curran Associates Inc."},{"key":"224_CR43","doi-asserted-by":"crossref","unstructured":"Wang, C. (2018). Interpreting neural network hate speech classifiers. In Workshop on abusive language online (ALW2) (pp. 86\u201392). Brussels, Belgium: Association for Computational Linguistics.","DOI":"10.18653\/v1\/W18-5111"},{"key":"224_CR44","doi-asserted-by":"crossref","unstructured":"Waseem, Z. (2016). Are you a racist or am I seeing things? Annotator influence on hate speech detection on Twitter. In Workshop on NLP and computational social science (pp. 138\u2013142). Austin, TX, USA: Association for Computational Linguistics.","DOI":"10.18653\/v1\/W16-5618"},{"key":"224_CR45","doi-asserted-by":"crossref","unstructured":"Waseem, Z., & Hovy, D. (2016). Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In NAACL student research workshop (pp. 88\u201393). Association for Computational Linguistics.","DOI":"10.18653\/v1\/N16-2013"},{"key":"224_CR46","doi-asserted-by":"publisher","unstructured":"Waseem, Z., Thorne, J., & Bingel, J. (2018). Bridging the gaps: Multi task learning for domain transfer of hate speech detection. Online Harassment (pp. 29\u201355). Cham: Springer. https:\/\/doi.org\/10.1007\/978-3-319-78583-7_3","DOI":"10.1007\/978-3-319-78583-7_3"},{"key":"224_CR47","doi-asserted-by":"crossref","unstructured":"Wulczyn, E., Thain, N., & Dixon, L. (2017). Ex machina: Personal attacks seen at scale. In World Wide Web, WWW \u201917 (pp. 1391\u20131399). ACM Press.","DOI":"10.1145\/3038912.3052591"},{"key":"224_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Robinson, D., & Tepper, J. (2018). Detecting hate speech on Twitter using a convolution-GRU based deep neural network. In A. Gangemi, R. Navigli, M. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, M. Alam (Eds.), ESWC 2018: The semantic web (pp. 745\u2013760).","DOI":"10.1007\/978-3-319-93417-4_48"}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-023-00224-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42001-023-00224-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-023-00224-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T08:23:45Z","timestamp":1701073425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42001-023-00224-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["224"],"URL":"https:\/\/doi.org\/10.1007\/s42001-023-00224-9","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"27 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}