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Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data

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Abstract

Automatic detection of cyberbullying in social media content is a natural language understanding and generic text classification task. The cultural diversities, country-specific trending topics hash-tags on social media, the unconventional use of typographical resources such as capitals, punctuation, emojis and easy availability of native language keyboards add to the variety and volume of user-generated content compounding the linguistic challenges. This research focuses on cyberbullying detection in the code-mix data, specifically the Hinglish, which refers to the juxtaposition of words from the Hindi and English languages. We explore the problem of cyberbullying prediction and propose MIIL-DNN, a multi-input integrative learning model based on deep neural networks. MIIL-DNN combines information from three sub-networks to detect and classify bully content in real-time code-mix data. It takes three inputs, namely English language features, Hindi language features (transliterated Hindi converted to the Hindi language) and typographic features, which are learned separately using sub-networks (capsule network for English, bi-LSTM for Hindi and MLP for typographic). These are then combined into one unified representation to be used as the input for a final regression output with linear activation. The advantage of using this model-level multi-lingual fusion is that it operates with the unique distribution of each input type without increasing the dimensionality of the input space. The robustness of the technique is validated on two datasets created by scraping data from the popular social networking sites, namely Twitter and Facebook. Experimental evaluation reveals that MIIL-DNN achieves superlative performance in terms of AUC-ROC curve on both the datasets.

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Notes

  1. https://www.firstsiteguide.com/cyberbullying-stats/.

  2. https://www.pewresearch.org/internet/wp-content/uploads/sites/9/media/Files/Reports/2007/PIP-Cyberbullying-Memo.pdf.pdf.

  3. https://www.cyberbullying.org/.

  4. https://www.unicef.org/press-releases/unicef-poll-more-third-young-people-30-countries-report-being-victim-online-bullying.

  5. https://www.ditchthelabel.org/wp-content/uploads/2017/07/The-Annual-Bullying-Survey-2017-1.pdf.

  6. https://www.en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers.

  7. Google Input Tools: https://www.google.co.in/inputtools/try/.

  8. SMS Dictionary. Vodacom Messaging. Retrieved 16 March 2012.

  9. https://www.emojipedia.org/.

  10. https://www.nltk.org/.

  11. https://www.cs.cmu.edu/~biglou/resources/bad-words.txt.

  12. https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge.

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  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

    Akshi Kumar & Nitin Sachdeva

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Kumar, A., Sachdeva, N. Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimedia Systems 28, 2027–2041 (2022). https://doi.org/10.1007/s00530-020-00672-7

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