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PEACE: Cross-Platform Hate Speech Detection - A Causality-Guided Framework

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14169))

Abstract

Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, due to the lack of labeled data in some platforms it becomes challenging to build hate speech detection models. To this end, we revisit if we can learn a generalizable hate speech detection model for the cross platform setting, where we train the model on the data from one (source) platform and generalize the model across multiple (target) platforms. Existing generalization models rely on linguistic cues or auxiliary information, making them biased towards certain tags or certain kinds of words (e.g., abusive words) on the source platform and thus not applicable to the target platforms. Inspired by social and psychological theories, we endeavor to explore if there exist inherent causal cues that can be leveraged to learn generalizable representations for detecting hate speech across these distribution shifts. To this end, we propose a causality-guided framework, PEACE, that identifies and leverages two intrinsic causal cues omnipresent in hateful content: the overall sentiment and the aggression in the text. We conduct extensive experiments across multiple platforms (representing the distribution shift) showing if causal cues can help cross-platform generalization.

P. Sheth and T. Kumarage—Both authors contributed equally. A. Chadha—Work does not relate to the position at Amazon.

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Notes

  1. 1.

    The code for PEACE can be accessed from: https://github.com/paras2612/PEACE.

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Acknowledgements

This material is based upon work supported by, or in part by the Office of Naval Research (ONR) under contract/grant number N00014-21-1-4002, the Army Research Office under the grant number W911NF2110030, and Defense Advanced Research Projects Agency (DARPA) under the grant number HR001120C0123. The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

Author information

Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Paaras Sheth, Tharindu Kumarage, Raha Moraffah & Huan Liu

  2. Stanford University, Stanford, CA, USA

    Aman Chadha

  3. Amazon Alexa AI, Sunnyvale, CA, USA

    Aman Chadha

Authors
  1. Paaras Sheth
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  2. Tharindu Kumarage
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  3. Raha Moraffah
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  4. Aman Chadha
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  5. Huan Liu
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Corresponding author

Correspondence to Paaras Sheth .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

Ethics declarations

Ethical Statement

Freedom of Speech and Censorship. Our research aims to develop algorithms that can effectively identify and mitigate harmful language across multiple platforms. We recognize the importance of protecting individuals from the adverse effects of hate speech and the need to balance this with upholding free speech. Content moderation is one application where our method could help censor hate speech on social media platforms such as Twitter, Facebook, Reddit, etc. However, one ethical concern is our system’s false positives, i.e., if the system incorrectly flags a user’s text as hate speech, it may censor legitimate free speech. Therefore, we discourage incorporating our methodology in a purely automated manner for any real-world content moderation system until and unless a human annotator works alongside the system to determine the final decision. Use of Hate Speech Datasets. In our work, we incorporated publicly available well-established datasets. We have correctly cited the corresponding dataset papers and followed the necessary steps in utilizing those datasets in our work. We understand that the hate speech examples used in the paper are potentially harmful content that could be used for malicious activities. However, our work aims to help better investigate and help mitigate the harms of online hate. Therefore, we have assessed that the benefits of using these real-world examples to explain our work better outweigh the potential risks. Fairness and Bias in Detection. Our work values the principles of fairness and impartiality. To reduce biases and ethical problems, we openly disclose our methodology, results, and limitations and will continue to assess and improve our system in the future.

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Sheth, P., Kumarage, T., Moraffah, R., Chadha, A., Liu, H. (2023). PEACE: Cross-Platform Hate Speech Detection - A Causality-Guided Framework. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_33

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  • DOI: https://doi.org/10.1007/978-3-031-43412-9_33

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