Mining Patterns of Cyberbullying on Twitter

Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non-stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbu...

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Bibliographic Details
Published in2017 IEEE International Conference on Data Mining Workshops (ICDMW) pp. 126 - 133
Main Authors Chelmis, Charalampos, Zois, Daphney-Stavroula, Mengfan Yao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
analysis
cyber aggression
cyber crime
Dictionaries
Feature extraction
Manuals
Network topology
online social networks
Tag clouds
Twitter
web mining
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Summary:Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non-stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large-scale real-world dataset to identify online social network topology structure features that are the most prominent in enhancing the accuracy of state-of-the-art classification methods for cyberbullying detection. We derive a small subset of features that are fast to compute while differentiating between "normal" users, cyberbullies and victims. Our findings have important implications for the design of future cyberbullying detection schemes.
ISSN:2375-9259
DOI:10.1109/ICDMW.2017.22