Reading List: Technical Case Studies

Articles about online misogyny, hate speech or harassment and their technical analysis.

Bouma, G., Ittoo, A., Basile, V., Metais, E., & Wortmann, H. (2014). Natural Language Processing and Information Systems. Data and Knowledge Engineering (Vol. 1). Springer International Publishing. https://doi.org/10.1007/978-3-319-91947-8

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1–15. https://doi.org/10.2147/OTT.S126905

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://arxiv.org/pdf/1608.07187.pdf

Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. http://arxiv.org/abs/1703.04009

Farrell, T., Fernandez, M., Novotny, J., & Alani, H. (2019). Exploring misogyny across the manosphere in reddit. WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science, 87–96. https://sci-hub.tw/10.1145/3292522.3326045

Frenda, S., Ghanem, B., Montes-Y-Gómez, M., & Rosso, P. (2019). Online hate speech against women: Automatic identification of misogyny and sexism on twitter. Journal of Intelligent and Fuzzy Systems, 36(5), 4743–4752. https://doi.org/10.3233/JIFS-179023

Geiger, R. S. (2016). Bot-based collective blocklists in Twitter: the counterpublic moderation of harassment in a networked public space. Information Communication and Society, 19(6), 787–803. https://doi.org/10.1080/1369118X.2016.1153700

Guberman, J., & Hemphill, L. (2017). Challenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets. Proceedings of the 50th Hawaii International Conference on System Sciences (2017), 2203–2212. https://doi.org/10.24251/hicss.2017.267

Guberman, J., Schmitz, C., & Hemphill, L. (2016). First steps in quantifying toxicity and verbal violence on Twitter. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 26-February-2016, 277–280. https://doi.org/10.1145/2818052.2869107

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems. https://arxiv.org/pdf/1610.02413.pdf

Hewitt, S., Tiropanis, T., & Bokhove, C. (2016). The problem of identifying misogynist language on Twitter (and other online social spaces). WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference, 333–335. https://doi.org/10.1145/2908131.2908183

Jhaver, S., Ghoshal, S., Bruckman, A., & Gilbert, E. (2018). Online harassment and content moderation: The case of blocklists. ACM Transactions on Computer-Human Interaction, 25(2), 1–33. https://doi.org/10.1145/3185593

Lingiardi, V. et al. (2019). Mapping Twitter hate speech towards social and sexual minorities: a lexicon-based approach to semantic content analysis. Behaviour and Information Technology, 0(0), 1–11. https://doi.org/10.1080/0144929X.2019.1607903

Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N., & Wojatzki, M. (2016). Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis. Proceedings of NLP4CMC III: 3rd Workshop on Natural Language Processing for Computer-Mediated Communication, 6–9. https://arxiv.org/pdf/1701.08118.pdf

Silva, L. et al. (2016). Analyzing the Targets of Hate in Online Social Media. AAAI ICWSM. https://arxiv.org/pdf/1603.07709.pdf

Waseem, Z., & Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. https://www.aclweb.org/anthology/N16-2013.pdf

Waseem, Z. (2016). Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter. https://www.aclweb.org/anthology/W16-5618.pdf

Wulczyn, E., Thain, N., & Dixon, L. (2016). Ex Machina: Personal Attacks Seen at Scale, 1–9. https://doi.org/10.1145/3038912.3052591

Xiang, G., Fan, B., Wang, L., Hong, J., & Rose, C. (2012). Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. ACM International Conference Proceeding Series, 1980–1984. https://doi.org/10.1145/2396761.2398556