Advertisement

Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

  • Sweta Agrawal
  • Amit AwekarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring (\(\sim \)12k posts), Twitter (\(\sim \)16k posts), and Wikipedia(\(\sim \)100k posts). Our experiments provide several useful insights about cyberbullying detection. To the best of our knowledge, this is the first work that systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning based models and transfer learning.

Keywords

Cyberbullying Social media Deep learning 

References

  1. 1.
    More experimental results. https://goo.gl/BBFxYH
  2. 2.
    Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: WWW, pp. 759–760 (2017)Google Scholar
  3. 3.
    Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., Bhamidipati, N.: Hate speech detection with comment embeddings. In: WWW, pp. 29–30 (2015)Google Scholar
  4. 4.
    Hinduja, S., Patchin, J.W.: Bullying, cyberbullying, and suicide. Arch. Suicide Res. 14(3), 206–221 (2010)CrossRefGoogle Scholar
  5. 5.
    Johnson, R., Zhang, T.: Supervised and semi-supervised text categorization using LSTM for region embeddings. In: ICML, pp. 526–534 (2016)Google Scholar
  6. 6.
    Karthik, D., Roi, R., Henry, L.: Modeling the detection of textual cyberbullying. In: Workshop on the Social Mobile Web, ICWSM (2011)Google Scholar
  7. 7.
    Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751 (2014)Google Scholar
  8. 8.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)zbMATHGoogle Scholar
  9. 9.
    Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., Chang, Y.: Abusive language detection in online user content. In: WWW, pp. 145–153 (2016)Google Scholar
  10. 10.
    Patchin, J.W., Hinduja, S.: Bullies move beyond the schoolyard: a preliminary look at cyberbullying. Youth Violence Juvenile Justice 4(2), 148–169 (2006)CrossRefGoogle Scholar
  11. 11.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)Google Scholar
  12. 12.
    Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: ICMLA, pp. 241–244 (2011)Google Scholar
  13. 13.
    Servance, R.L.: Cyberbullying, cyber-harassment, and the conflict between schools and the first amendment. Wis. Law Rev. 12–13 (2003)Google Scholar
  14. 14.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: ACL, pp. 1555–1565 (2014)Google Scholar
  15. 15.
    Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., Hoste, V.: Automatic detection and prevention of cyberbullying. In: International Conference Human and Social Analytics, pp. 13–18 (2015)Google Scholar
  16. 16.
    Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: NAACL SRW, pp. 88–93 (2016)Google Scholar
  17. 17.
    Whittaker, E., Kowalski, R.M.: Cyberbullying via social media. J. Sch. Violence 14(1), 11–29 (2015)CrossRefGoogle Scholar
  18. 18.
    Wulczyn, E., Thain, N., Dixon, L.: Ex machina: personal attacks seen at scale. In: WWW, pp. 1391–1399 (2017)Google Scholar
  19. 19.
    Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. In: The Workshop on Content Analysis in the WEB 2.0, WWW, pp. 1–7 (2009)Google Scholar
  20. 20.
    Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. In: COLING, pp. 3485–3495 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Member of Technical Staff, Adobe SystemsNoidaIndia
  2. 2.Indian Institute of Technology, GuwahatiGuwahatiIndia

Personalised recommendations