{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T22:20:40Z","timestamp":1775773240230,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s41060-023-00437-1","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T06:02:21Z","timestamp":1692165741000},"page":"445-455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing hate speech detection with user characteristics"],"prefix":"10.1007","volume":"18","author":[{"given":"Rohan","family":"Raut","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Spezzano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"437_CR1","doi-asserted-by":"publisher","first-page":"204951","DOI":"10.1109\/ACCESS.2020.3037073","volume":"8","author":"PK Roy","year":"2020","unstructured":"Roy, P.K., Tripathy, A.K., Das, T.K., Gao, X.-Z.: A framework for hate speech detection using deep convolutional neural network. IEEE Access 8, 204951\u2013204962 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3037073","journal-title":"IEEE Access"},{"key":"437_CR2","doi-asserted-by":"crossref","unstructured":"He, B., Ziems, C., Soni, S., Ramakrishnan, N., Yang, D., Kumar, S.: Racism is a virus: anti-asian hate and counterspeech in social media during the covid-19 crisis. In: Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 90\u201394 (2021)","DOI":"10.1145\/3487351.3488324"},{"key":"437_CR3","doi-asserted-by":"publisher","first-page":"7881","DOI":"10.1109\/ACCESS.2022.3143799","volume":"10","author":"S Khan","year":"2022","unstructured":"Khan, S., Kamal, A., Fazil, M., Alshara, M.A., Sejwal, V.K., Alotaibi, R.M., Baig, A.R., Alqahtani, S.: Hcovbi-caps: Hate speech detection using convolutional and bi-directional gated recurrent unit with capsule network. IEEE Access 10, 7881\u20137894 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3143799","journal-title":"IEEE Access"},{"key":"437_CR4","doi-asserted-by":"crossref","unstructured":"Waseem, Z., Hovy, D.: Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In: Proceedings of the NAACL Student Research Workshop, pp. 88\u201393. Association for Computational Linguistics, San Diego, California (2016). http:\/\/www.aclweb.org\/anthology\/N16-2013","DOI":"10.18653\/v1\/N16-2013"},{"key":"437_CR5","doi-asserted-by":"publisher","unstructured":"Fehn Unsv\u00e5g, E., Gamb\u00e4ck, B.: The effects of user features on Twitter hate speech detection. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pp. 75\u201385. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/W18-5110. https:\/\/aclanthology.org\/W18-5110","DOI":"10.18653\/v1\/W18-5110"},{"key":"437_CR6","doi-asserted-by":"publisher","unstructured":"Mosca, E., Wich, M., Groh, G.: Understanding and interpreting the impact of user context in hate speech detection. In: Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, pp. 91\u2013102. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.socialnlp-1.8. https:\/\/aclanthology.org\/2021.socialnlp-1.8","DOI":"10.18653\/v1\/2021.socialnlp-1.8"},{"issue":"1","key":"437_CR7","first-page":"647","volume":"1","author":"JY Kim","year":"2021","unstructured":"Kim, J.Y., Kesari, A.: Misinformation and hate speech: The case of anti-asian hate speech during the covid-19 pandemic. J. Online Trust Saf. 1(1), 647\u2013667 (2021)","journal-title":"J. Online Trust Saf."},{"key":"437_CR8","doi-asserted-by":"crossref","unstructured":"Li, J., Ning, Y.: Anti-asian hate speech detection via data augmented semantic relation inference. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, pp. 607\u2013617 (2022)","DOI":"10.1609\/icwsm.v16i1.19319"},{"key":"437_CR9","doi-asserted-by":"crossref","unstructured":"Xia, M., Field, A., Tsvetkov, Y.: Demoting racial bias in hate speech detection. CoRR arXiv:2005.12246 (2020)","DOI":"10.18653\/v1\/2020.socialnlp-1.2"},{"key":"437_CR10","doi-asserted-by":"publisher","unstructured":"Davidson, T., Bhattacharya, D., Weber, I.: Racial bias in hate speech and abusive language detection datasets. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 25\u201335. Association for Computational Linguistics, Florence, Italy (2019). https:\/\/doi.org\/10.18653\/v1\/W19-3504. https:\/\/aclanthology.org\/W19-3504","DOI":"10.18653\/v1\/W19-3504"},{"key":"437_CR11","doi-asserted-by":"publisher","first-page":"598","DOI":"10.7717\/peerj-cs.598","volume":"7","author":"W Yin","year":"2021","unstructured":"Yin, W., Zubiaga, A.: Towards generalisable hate speech detection: a review on obstacles and solutions. PeerJ Computer Science 7, 598 (2021)","journal-title":"PeerJ Computer Science"},{"key":"437_CR12","doi-asserted-by":"publisher","unstructured":"Ding, Y., Zhou, X., Zhang, X.: YNU_DYX at SemEval-2019 task 5: A stacked BiGRU model based on capsule network in detection of hate. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 535\u2013539. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019). https:\/\/doi.org\/10.18653\/v1\/S19-2096. https:\/\/aclanthology.org\/S19-2096","DOI":"10.18653\/v1\/S19-2096"},{"key":"437_CR13","doi-asserted-by":"publisher","first-page":"106363","DOI":"10.1109\/ACCESS.2021.3100435","volume":"9","author":"HS Alatawi","year":"2021","unstructured":"Alatawi, H.S., Alhothali, A.M., Moria, K.M.: Detecting white supremacist hate speech using domain specific word embedding with deep learning and bert. IEEE Access 9, 106363\u2013106374 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3100435","journal-title":"IEEE Access"},{"key":"437_CR14","doi-asserted-by":"publisher","unstructured":"Gamb\u00e4ck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. In: Proceedings of the First Workshop on Abusive Language Online, pp. 85\u201390. Association for Computational Linguistics, Vancouver, BC, Canada (2017). https:\/\/doi.org\/10.18653\/v1\/W17-3013 . https:\/\/aclanthology.org\/W17-3013","DOI":"10.18653\/v1\/W17-3013"},{"key":"437_CR15","doi-asserted-by":"publisher","unstructured":"Park, J.H., Fung, P.: One-step and Two-step Classification for Abusive Language Detection on Twitter. arXiv (2017). https:\/\/doi.org\/10.48550\/ARXIV.1706.01206 . https:\/\/arxiv.org\/abs\/1706.01206","DOI":"10.48550\/ARXIV.1706.01206"},{"key":"437_CR16","unstructured":"Irani, D., Wrat, A., Amir, S.: Early detection of online hate speech spreaders with learned user representations (2021)"},{"key":"437_CR17","unstructured":"Rangel, F., Sarrac\u00e9n, G., Chulvi, B., Fersini, E., Rosso, P.: Profiling hate speech spreaders on twitter task at pan 2021. In: CLEF (2021)"},{"key":"437_CR18","unstructured":"Dukic, D., Kr\u017eic, A.S.: Detection of hate speech spreaders with bert (2021)"},{"key":"437_CR19","doi-asserted-by":"publisher","unstructured":"Ribeiro, M., Calais, P., Santos, Y., Almeida, V., Meira Jr., W.: Characterizing and detecting hateful users on twitter. Proceedings of the International AAAI Conference on Web and Social Media 12(1), (2018). https:\/\/doi.org\/10.1609\/icwsm.v12i1.15057","DOI":"10.1609\/icwsm.v12i1.15057"},{"key":"437_CR20","doi-asserted-by":"crossref","unstructured":"Qian, J., ElSherief, M., Belding, E.M., Wang, W.Y.: Leveraging intra-user and inter-user representation learning for automated hate speech detection. arXiv preprint arXiv:1804.03124 (2018)","DOI":"10.18653\/v1\/N18-2019"},{"key":"437_CR21","unstructured":"Mishra, P., Tredici, M.D., Yannakoudakis, H., Shutova, E.: Abusive language detection with graph convolutional networks. In: North American Chapter of the Association for Computational Linguistics (2019)"},{"issue":"1","key":"437_CR22","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s13278-023-01051-6","volume":"13","author":"S Nagar","year":"2023","unstructured":"Nagar, S., Barbhuiya, F.A., Dey, K.: Towards more robust hate speech detection: using social context and user data. Soc. Netw. Anal. Min. 13(1), 47 (2023)","journal-title":"Soc. Netw. Anal. Min."},{"key":"437_CR23","doi-asserted-by":"crossref","unstructured":"Founta, A.M., Djouvas, C., Chatzakou, D., Leontiadis, I., Blackburn, J., Stringhini, G., Vakali, A., Sirivianos, M., Kourtellis, N.: Large scale crowdsourcing and characterization of twitter abusive behavior. In: Twelfth International AAAI Conference on Web and Social Media (2018)","DOI":"10.1609\/icwsm.v12i1.14991"},{"key":"437_CR24","unstructured":"Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of liwc2015. Technical report (2015)"},{"key":"437_CR25","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"437_CR26","doi-asserted-by":"crossref","unstructured":"Chen, L., Lyu, H., Yang, T., Wang, Y., Luo, J.: In the eyes of the beholder: Sentiment and topic analyses on social media use of neutral and controversial terms for covid-19. arXiv preprint arXiv:2004.10225, 1\u20138 (2020)","DOI":"10.1007\/978-3-030-80387-2_6"},{"key":"437_CR27","doi-asserted-by":"publisher","first-page":"204951","DOI":"10.1109\/ACCESS.2020.3037073","volume":"8","author":"PK Roy","year":"2020","unstructured":"Roy, P.K., Tripathy, A.K., Das, T.K., Gao, X.-Z.: A framework for hate speech detection using deep convolutional neural network. IEEE Access 8, 204951\u2013204962 (2020)","journal-title":"IEEE Access"},{"key":"437_CR28","unstructured":"Nagar, S., Gupta, S., Bahushruth, C., Barbhuiya, F.A., Dey, K.: Empirical assessment and characterization of homophily in classes of hate speeches. In: AffCon@ AAAI, pp. 30\u201334 (2021)"},{"key":"437_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hale, S., Adelani, D.I., Grabowicz, P., Hartman, T., Fl\u00f6ck, F., Jurgens, D.: Demographic inference and representative population estimates from multilingual social media data. In: The World Wide Web Conference, pp. 2056\u20132067 (2019)","DOI":"10.1145\/3308558.3313684"},{"key":"437_CR30","unstructured":"Mohammad, S.: Word affect intensities. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan (2018). https:\/\/aclanthology.org\/L18-1027"},{"key":"437_CR31","doi-asserted-by":"crossref","unstructured":"Milton, A., Batista, L., Allen, G., Gao, S., Ng, Y.-K.D., Pera, M.S.: \u201cdon\u2019t judge a book by its cover\u201d: Exploring book traits children favor. In: Fourteenth ACM Conference on Recommender Systems, pp. 669\u2013674 (2020)","DOI":"10.1145\/3383313.3418490"},{"key":"437_CR32","unstructured":"Milton, A., Pera, M.S.: What snippets feel: Depression, search, and snippets (2020)"},{"key":"437_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42460-6","volume-title":"Computational Personality Analysis: Introduction, Practical Applications and Novel Directions","author":"Y Neuman","year":"2016","unstructured":"Neuman, Y.: Computational Personality Analysis: Introduction, Practical Applications and Novel Directions. Springer, Cham (2016)"},{"issue":"2","key":"437_CR34","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MIS.2017.23","volume":"32","author":"N Majumder","year":"2017","unstructured":"Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74\u201379 (2017)","journal-title":"IEEE Intell. Syst."},{"issue":"2","key":"437_CR35","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0191-8869(96)00033-5","volume":"21","author":"A Furnham","year":"1996","unstructured":"Furnham, A.: The big five versus the big four: the relationship between the Myers\u2013Briggs type indicator (MBTI) and neo-pi five factor model of personality. Pers. Individ. Differ. 21(2), 303\u2013307 (1996)","journal-title":"Pers. Individ. Differ."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-023-00437-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-023-00437-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-023-00437-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T02:10:59Z","timestamp":1728439859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-023-00437-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,16]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["437"],"URL":"https:\/\/doi.org\/10.1007\/s41060-023-00437-1","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,16]]},"assertion":[{"value":"15 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 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":"The authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}