Abstract
AbstractToday, 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—dubbed the Map of Hate—in 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.
Funder
University of Technology Sydney
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Transportation
Reference48 articles.
1. 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
2. Awal, M. R., Cao, R., Lee, R. KW., & Mitrović, 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
3. Badjatiya, P., Gupta, M., & Varma, V. (2019). Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. In WWW’19 (pp. 49–59). ACM Press.
4. Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017). Deep learning for hate speech detection in tweets. In WWW ’17 (pp. 759–760). ACM Press.
5. 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. 99–124). American Psychological Association.