Abstract
Hate speech is a prevalent and pervasive phenomenon on social media platforms. Detecting hate speech and modelling its spread is a significant research problem with practical implications. Though hate speech detection has been an active area of research, hate spread modelling is still in its nascent stage. Prior works have analyzed the hateful users’ social network embedding, hateful user detection using belief propagation models, and the spread velocity of hateful content. However, these prior works fail to factor in the multiple hateful forms (such as hate against gender, race and ethnicity) and the temporal evolution of hate spread, limiting their applicability. We take a holistic approach wherein we model the spread of hate as a single form and fine-granular spread of hateful forms. We extend the traditional spread and activation (SPA) model to capture the spread of hate and its forms. We use SPA to model, the spread of hate as one single form while TopSPA captures the spread of multiple hate forms. We also propose ways to detect hateful forms by using latent topics present in hateful content. We empirically demonstrate our approach to a dataset from Twitter that contains ample hate speech instances along with users labelled as hateful or not.
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This implies removing the terms that appear in less than 3 documents.
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This implies removing the terms which appear in more than \(90\%\) of the documents.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Eleventh International AAAI Conference on Web and Social Media (2017)
Dey, K., Lamba, H., Nagar, S., Gupta, S., Kaushik, S.: Modeling topical information diffusion over microblog networks. In: International Conference on Complex Networks and their Applications, pp. 353–364. Springer (2018)
Golub, B., Jackson, M.O.: Naive learning in social networks and the wisdom of crowds. Am. Econ. J.: Microecon. 2(1), 112–49 (2010)
Li, M., Wang, X., Gao, K., Zhang, S.: A survey on information diffusion in online social networks: Models and methods. Information 8(4), 118 (2017)
Mathew, B., Dutt, R., Goyal, P., Mukherjee, A.: Spread of hate speech in online social media. In: Proceedings of the 10th ACM Conference on Web Science
Mathew, B., Kumar, N., Goyal, P., Mukherjee, A., et al.: Analyzing the hate and counter speech accounts on Twitter. arXiv preprint arXiv:1812.02712 (2018)
Ratadiya, P., Mishra, D.: An attention ensemble based approach for multilabel profanity detection. In: 2019 International Conference on Data Mining Workshops (ICDMW), pp. 544–550 (2019)
Ribeiro, M.H., Calais, P.H., Santos, Y.A., Almeida, V.A., Meira Jr, W.: “Like sheep among wolves": characterizing hateful users on Twitter. arXiv preprint arXiv:1801.00317 (2017)
Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399–408 (2015)
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704 (2011)
Wang, C., Yang, X.Y., Xu, K., Ma, J.: SEIR-based model for the information spreading over SNS. Acta Electronica Sinica 11, 031 (2014)
Wang, Q., Lin, Z., Jin, Y., Cheng, S., Yang, T.: ESIS: emotion-based spreader–ignorant–Stifler model for information diffusion. Knowl.-Based Syst
Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pp. 90–94 (2012)
Xuejun, D.: Research on propagation model of public opinion topics based on SCIR in microblogging. Comput. Eng. Appl. 51(8), 20–26 (2015)
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Nagar, S., Gupta, S., Barbhuiya, F.A., Dey, K. (2022). Capturing the Spread of Hate on Twitter Using Spreading Activation Models. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_2
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DOI: https://doi.org/10.1007/978-3-030-93413-2_2
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