Flexible inference for cyberbully incident detection

Haoti Zhong; David J. Miller; Anna Squicciarini

doi:10.1007/978-3-030-10997-4_22

Flexible inference for cyberbully incident detection

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
Editors	Ulf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
Publisher	Springer Verlag
Pages	356-371
Number of pages	16
ISBN (Print)	9783030109967
DOIs	https://doi.org/10.1007/978-3-030-10997-4_22
State	Published - 2019
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland Duration: Sep 10 2018 → Sep 14 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11053 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
Country/Territory	Ireland
City	Dublin
Period	9/10/18 → 9/14/18

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-10997-4_22

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Zhong, H., Miller, D. J., & Squicciarini, A. (2019). Flexible inference for cyberbully incident detection. In U. Brefeld, A. Marascu, F. Pinelli, E. Curry, B. MacNamee, N. Hurley, E. Daly, & M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 356-371). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_22

Zhong, Haoti ; Miller, David J. ; Squicciarini, Anna. / Flexible inference for cyberbully incident detection. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. editor / Ulf Brefeld ; Alice Marascu ; Fabio Pinelli ; Edward Curry ; Brian MacNamee ; Neil Hurley ; Elizabeth Daly ; Michele Berlingerio. Springer Verlag, 2019. pp. 356-371 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Flexible inference for cyberbully incident detection",

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{\textquoteright} comments largely independently in the front-end layers, but while also accounting for possible conversational patterns. The front-end layer{\textquoteright}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.",

author = "Haoti Zhong and Miller, \{David J.\} and Anna Squicciarini",

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doi = "10.1007/978-3-030-10997-4\_22",

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editor = "Ulf Brefeld and Alice Marascu and Fabio Pinelli and Edward Curry and Brian MacNamee and Neil Hurley and Elizabeth Daly and Michele Berlingerio",

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Zhong, H, Miller, DJ & Squicciarini, A 2019, Flexible inference for cyberbully incident detection. in U Brefeld, A Marascu, F Pinelli, E Curry, B MacNamee, N Hurley, E Daly & M Berlingerio (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11053 LNAI, Springer Verlag, pp. 356-371, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018, Dublin, Ireland, 9/10/18. https://doi.org/10.1007/978-3-030-10997-4_22

Flexible inference for cyberbully incident detection. / Zhong, Haoti; Miller, David J.; Squicciarini, Anna.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. ed. / Ulf Brefeld; Alice Marascu; Fabio Pinelli; Edward Curry; Brian MacNamee; Neil Hurley; Elizabeth Daly; Michele Berlingerio. Springer Verlag, 2019. p. 356-371 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Zhong H, Miller DJ , Squicciarini A. Flexible inference for cyberbully incident detection. In Brefeld U, Marascu A, Pinelli F, Curry E, MacNamee B, Hurley N, Daly E, Berlingerio M, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Springer Verlag. 2019. p. 356-371. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-10997-4_22

Flexible inference for cyberbully incident detection

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