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Detecting Cyberbullying Activity Across Platforms

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1134)


This article focuses on cyber security issues related to content. Recent surveys show that youth are being exposed to cyber-aggression at increasing rates, with over 43% of youth reporting in one recent survey that they have been bullied online. While research into this problem has been growing, the research community is hampered by a lack of authentic data for studying communication with and among youth. A large corpus with 800,000 instances of cell phone textual data from youth ages 10–14 has been developed to address this need. This article describes the dataset, as well as plans to enable access to the data while protecting the privacy of the study participants. The results from machine learning experiments for the detection of cyberbullying based on labeled data from several sources, including both SMS and social media messages, are also discussed. These algorithms are shown to be effective at detecting cyberbullying across platforms.


  • Cybersafety
  • Cyberbullying detection
  • Machine learning
  • Information retrieval
  • Human factors in cybersecurity

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This material is based upon work supported in part by the National Science Foundation under Grant Nos. 0916152, 1812380 and 1421896. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Correspondence to April Edwards .

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Edwards, A., Demoll, D., Edwards, L. (2020). Detecting Cyberbullying Activity Across Platforms. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham.

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