Flexible inference for cyberbully incident detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We study detection of cyberbully incidents in online social networks, focusing on session level analysis. We propose several variants of a customized convolutional neural networks (CNN) approach, which processes users’ comments largely independently in the front-end layers, but while also accounting for possible conversational patterns. The front-end layer’s outputs are then combined by one of our designed output layers – namely by either a max layer or by a novel sorting layer, proposed here. Our CNN models outperform existing baselines and are able to achieve classification accuracy of up to 84.29% for cyberbullying and 83.08% for cyberaggression.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
EditorsUlf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
PublisherSpringer Verlag
Pages356-371
Number of pages16
ISBN (Print)9783030109967
DOIs
StatePublished - Jan 1 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland
Duration: Sep 10 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11053 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
CountryIreland
CityDublin
Period9/10/189/14/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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