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Tracking Hate in Social Media: Evaluation, Challenges and Approaches

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Abstract

This paper presents online hate speech as a societal and computational challenge. Offensive content detection in social media is considered as a multilingual, multi-level, multi-class classification problem for three Indo-European languages. This research problem is offered to the community through the HASOC shared task. HASOC intends to stimulate research and development in hate speech recognition across different languages. Three datasets (in English, German, and Hindi) were developed from Twitter and Facebook, and made available. This paper describes the creation of the multilingual datasets and the annotation method. We will present the numerous approaches based on traditional classifiers, deep neural models, and transfer learning models, along with features used for the classification. Results show that the best classifier for the binary classification might not perform best in the multi-class classification, and the performance of the same classifier varies across the languages. Overall, transfer learning models such as BERT, and deep neural models based on LSTMs and CNNs perform similar but better than traditional classifiers such as SVM. We will conclude the discussion with a list of issues that needs to be addressed for future datasets.

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Notes

  1. https://www.facebook.com/communitystandards/.

  2. https://hasoc2019.github.io/.

  3. https://projects.fzai.h-da.de/iggsa/germeval/.

  4. https://competitions.codalab.org/competitions/19935.

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Acknowledgements

We would like to acknowledge Mr. Chintak Mandlia, Mohana Dave, Aditya Patel for the help in managing the HASOC track. We are also thankful to the college junior students who helped us to annotate the dataset.

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  1. DA-IICT, Gandhinagar, India

    Sandip Modha & Prasenjit Majumder

  2. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

  3. LDRP-ITR, Gandhinagar, India

    Sandip Modha & Daksh Patel

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This article is part of the topical collection “Forum for Information Retrieval Evaluation” guest edited by Mandar Mitra and Prasenjit Majumder.

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Modha, S., Mandl, T., Majumder, P. et al. Tracking Hate in Social Media: Evaluation, Challenges and Approaches. SN COMPUT. SCI. 1, 105 (2020). https://doi.org/10.1007/s42979-020-0082-0

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