Publications: Hate speech
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Workneh, T.W., 'Ethiopia’s Hate Speech Predicament: Seeking Antidotes Beyond a Legislative Response' (2020), African Journalism Studies [online]
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Wright, H., 'The mythology of “freedom of speech”: Bullwhips or just bull?' (1995), Polemic, 6(1), 8
Wu, C.S. & Bhandary, U., 'Detection of Hate Speech in Videos Using Machine Learning' (2020), Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, 9458005, pp. 585-590
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Wu, F., Gao, B., Pan, X., Li, L., Ma, Y., Liu, S. & Liu, Z., 'Fuser: An enhanced multimodal fusion framework with congruent reinforced perceptron for hateful memes detection' (2024), Information Processing and Management, 61(4), 103772
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Wu, F., Lakhanpal, S., Li, Q., Lee, K., Kim, D., Chae, H. & Kwon, K.H., 'Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social Media' (2024), WWW 2024 - Proceedings of the ACM Web Conference, pp. 2615–2626
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Wu, J., Zhang, C., Liu, Z., Zhang, E., Wilson, S. & Zhang, C., 'GraphBERT: Bridging Graph and Text for Malicious Behavior Detection on Social Media' (2022), Proceedings - IEEE International Conference on Data Mining, ICDM, 2022-November, pp. 548-557
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Wu, Q. & Semaan, B., 'How Do You Quantify How Racist Something Is?: Color-Blind Moderation in Decentralized Governance' (2023), Proceedings of the ACM on Human-Computer Interaction, 7(CSCW2), 3610030
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Wu, X.-K., Zhao, T.-F., Lu, L. & Chen, W.-N., 'Predicting the Hate: A GSTM Model based on COVID-19 Hate Speech Datasets' (2022), Information Processing and Management, 59(4), 102998
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Wullach, T., Adler, A. & Minkov, E., 'Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech' (2021), Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pp. 4699-4705
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Wullach, T., Adler, A. & Minkov, E.M., 'Towards Hate Speech Detection at Large via Deep Generative Modeling' (2020), IEEE Internet Computing [online]
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Wypych, M. & Bilewicz, M., 'Psychological toll of hate speech: The role of acculturation stress in the effects of exposure to ethnic slurs on mental health among Ukrainian immigrants in Poland' (2022), Cultural Diversity and Ethnic Minority Psychology [online]
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Xavier, H.S., 'Violentometer: measuring violence on the Web in real time' (2023), ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023, pp. 272-275
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Xia, C. & Johnson, N.F., 'Nonlinear spreading behavior across multi-platform social media universe' (2024), Chaos, 34(4), 043149
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Xiang, K., Zhang, Z., Yu, Y., San Lucas, L., Amin, M.R. & Li, Y., 'Identification of Hate Tweets: Which Words Matter the Most?' (2021), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13095 LNCS, pp. 586-598
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Xie, C., Liu, P. & Cheng, Y., 'Praxis, hashtag activism, and social justice: a content analysis of #StopAsianHate narratives' (2023), Asian Journal of Communication [online]
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Xin, W., 'Censorship Bubbles Vs Hate Bubbles' (2023), Social Epistemology [online]
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XinYing, C., Tiberius, V., Alnoor, A., Camilleri, M. & Khaw, K.W., 'The Dark Side of Metaverse: A Multi-Perspective of Deviant Behaviors From PLS-SEM and fsQCA Findings' (2024), International Journal of Human-Computer Interaction [online]
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Xiong, H., Jiao, W. & Cai, Y., 'TCE-DBF: textual context enhanced dynamic bimodal fusion for hate video detection' (2024), Data Technologies and Applications [online]
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Xu, C. & Li, J., 'Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification' (2022), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, pp. 5644-5656
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Xu, D., Yuan, S., Wang, Y., Nwude, A.U., Zhang, L., Zajicek, A. & Wu, X., 'Coded Hate Speech Detection via Contextual Information' (2022), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13280 LNAI, pp. 93-105
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Yacoub, M., 'Reiterating Anti-Muslim Rhetoric: Interview Practices of Fox News’ Hannity' (2022), International Journal of Islamic Thought, 21, pp. 83-92
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Yadav, A.K., Kumar, M., Kumar, A., Shivani, Kusum & Yadav, D., 'Hate speech recognition in multilingual text: Hinglish documents' (2023), International Journal of Information Technology (Singapore) [online]
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Yadav, P., Kaur, R. & Kumar, A., 'A Study on Social Media Sentimental Analysis in Hindi Language' (2023), Proceedings of the International Conference on Circuit Power and Computing Technologies, ICCPCT 2023, pp. 1395–1400
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Yadav, P., Kiran Kumar Reddy, G., Erukala, S., Bhaskar, B., Komuravelly, S.K. & Kasanagottu, S., 'The automatic racism text detection on social media data using machine learning' (2024), AIP Conference Proceedings, 2971(1), 020001
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Yadav, S., 'Explainable and Debiased Misogyny Identification in Code-Mixed Hinglish using Artificial Intelligence Models' (2024), CEUR Workshop Proceedings, 3793, pp. 457–464
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Yadav, S., Kaushik, A. & McDaid, K., 'Hate Speech is not Free Speech: Explainable Machine Learning for Hate Speech Detection in Code-Mixed Languages' (2023), 2023 IEEE International Symposium on Technology and Society (ISTAS) Proceedings
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Yadav, S., Kaushik, A. & McDaid, K., 'Understanding Interpretability: Explainable AI Approaches for Hate Speech Classifiers' (2023), Communications in Computer and Information Science, 1903 CCIS, pp. 47–70
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Yadav, Y., Bajaj, P., Gupta, R.K. & Sinha, R., 'A Comparative Study of Deep Learning Methods for Hate Speech and Offensive Language Detection in Textual Data' (2021), Proceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021
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Yamini, R., Ashwin, G. & Prasad, S.H., 'Sentiment Analysis on YouTube Comments Using Feed Forward Neural Network' (2024), AIP Conference Proceedings, 3075(1), 020109
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Yanaputeranto, M.R. & Setiawan, E.B., 'Detection and Classification of Hate Speech on Twitter Using Convolutional Neural Network (CNN)' (2024), 2024 International Conference on Data Science and Its Applications, ICoDSA 2024, pp. 340–345
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Yang, C., Liu, Y., Zhu, F., Han, J. & Hu, S., 'Uncertainty-Guided Modal Rebalance for Hateful Memes Detection' (2024), Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, pp. 4361–4371
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Yang, J., Wu, X. & Kong, J., 'A Study of Speech Emotion Change Based on the Speech Emotion Evaluation Scale (SEES) Model' (2024), ACM International Conference Proceeding Series, pp. 235–240
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Yang, K., Jang, W. & Cho, W.I., 'APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets' (2022), Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 7105-7115
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Yang, M., Kiang, M., Ku, Y., Chiu, C. & Li, Y., 'Social Media Analytics for Radical Opinion Mining in Hate Group Web Forums' (2020), Journal of Homeland Security and Emergency Management, 8(1), 38
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Yaosheng, Z., Tiegang, Z., Tingjun, Y. & Li, H., 'Domain-enhanced Prompt Learning for Chinese Implicit Hate Speech Detection' (2024), IEEE Access [online]
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Yeasin Ramadan, S.T., Sakib, T., Rahat, M.A., Mushfique Hossain, M., Rahman, R. & Rahman, M.M., 'An Integrated Embedded System Towards Abusive Bengali Speech and Speaker Detection Using NLP and Deep Learning' (2022), Proceedings of 2022 25th International Conference on Computer and Information Technology, ICCIT 2022, pp. 698-703
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Yigezu, M.G., Kolesnikova, O., Sidorov, G. & Gelbukh, A., 'Transformer-Based Hate Speech Detection for Multi-Class and Multi-Label Classification' (2023), Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023) co-located with the Conference of the Spanish Society for Natural Language Processing (SEPLN 2023)
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Yildirim, M.M., Nagler, J., Bonneau, R. & Tucker, J.A., 'Short of Suspension: How Suspension Warnings Can Reduce Hate Speech on Twitter' (2021), Perspectives on Politics [online]
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Yılmaz, F., Elmas, T. & Eröz, B., 'Twitter-based analysis of anti-refugee discourses in Türkiye' (2023), Discourse and Communication, 17(3), pp. 298-318
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Yin, W. & Zubiaga, A., 'Towards generalisable hate speech detection: A review on obstacles and solutions' (2021), PeerJ Computer Science, 7, pp. 1-38
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Yin, Z., Fan, L., Yu, H. & Gilliland, A.J., 'Using a Three-step Social Media Similarity (TSMS) Mapping Method to Analyze Controversial Speech Relating to COVID-19 in Twitter Collections' (2020), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 9377930, pp. 1949-1953
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Yoder, M.M., Ng, L.H.X., Brown, D.W. & Carley, K.M., 'How Hate Speech Varies by Target Identity: A Computational Analysis' (2022), CoNLL 2022 - 26th Conference on Computational Natural Language Learning, Proceedings of the Conference, pp. 27-39
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Yoder, M.M., Perry, C., Brown, D.W., Carley, K.M. & Pruden, M., 'Identity Construction in a Misogynist Incels Forum' (2023), Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 1–13
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Yong, C., 'Does freedom of speech include hate speech?' (2011), Res Publica, 17(4), pp.385-403
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Younes, M., Kharabsheh, A. & Younes, M.B., 'Alexa at SemEval-2023 Task 10: Ensemble Modeling of DeBERTa and BERT Variations for Identifying Sexist Text' (2023), 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop, pp. 1644–1649
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Young, G.K., 'How much is too much: The difficulties of social media content moderation' (2021), Information and Communications Technology Law [online]
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Younus, A. & Qureshi, M.A., 'A Framework for Sexism Detection on Social Media via ByT5 and TabNet' (2022), CEUR Workshop Proceedings, 3202
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Yousefi, N., Noor, N.B., Spann, B. & Agarwal, N., 'Examining Toxicity’s Impact on Reddit Conversations' (2024), Studies in Computational Intelligence, 1144 SCI, pp. 401–411
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Yu, S., Song, S. & Kim, Y., 'Rational Text Augmentation Method with Korean Misspellings' (2024), Digest of Technical Papers - IEEE International Conference on Consumer Electronics
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Yu, X., Blanco, E. & Hong, L., 'Hate Speech and Counter Speech Detection: Conversational Context Does Matter' (2022), NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, pp. 5918-5930
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