Optimal Online Cyberbullying Detection

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and t...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2017 - 2021
Main Authors Zois, Daphney-Stavroula, Kapodistria, Angeliki, Yao, Mengfan, Chelmis, Charalampos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
classification
cyber harassment
Feature extraction
Optimization
optimization algorithm
Random variables
Scalability
selection process
social media
Testing
Twitter
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Summary:Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462092