Scalable and timely detection of cyberbullying in online social networks

Rahat Ibn Rafiq, Homa Hosseinmardi, Richard Han, Qin Lv, Shivakant Mishra

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

38 Citations (Scopus)


Cyberbullying in Online Social Networks (OSNs) has grown to be a serious problem among teenagers. While a considerable amount of research has been conducted focusing on designing highly accurate classifiers to automatically detect cyberbullying instances in OSNs, two key practical issues remain to be worked upon, namely scalability of a cyberbullying detection system and timeliness of raising alerts whenever cyberbullying occurs. These two issues form the motivation of our work. We propose a multi-stage cyberbullying detection solution that drastically reduces the classification time and the time to raise alerts. The proposed system is highly scalable without sacrificing accuracy and highly responsive in raising alerts. The design is comprised of two novel components, a dynamic priority scheduler and an incremental classification mechanism. We have implemented this solution, and using data obtained from Vine, we conducted a thorough performance evaluation to demonstrate the utility and scalability of each of these components. We show that our complete solution is significantly more scalable and responsive than the current state of the art.

Original languageEnglish
Title of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450351911
Publication statusPublished - 2018
Externally publishedYes
Event33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France
Duration: 9 Apr 201813 Apr 2018

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference33rd Annual ACM Symposium on Applied Computing, SAC 2018


  • Cyberbullying
  • Scalable systems
  • Social networks


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