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Flexible Inference for Cyberbully Incident Detection

  • Haoti Zhong
  • David J. MillerEmail author
  • Anna Squicciarini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

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.

Notes

Acknowledgements

Work from Dr. Squicciarini and Haoti Zhong was partly supported by the National Science Foundation under Grant 1453080 and Grant 1421776.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haoti Zhong
    • 1
  • David J. Miller
    • 1
    Email author
  • Anna Squicciarini
    • 2
  1. 1.Electrical Engineering DepartmentPennsylvania State UniversityState CollegeUSA
  2. 2.College of Information Sciences and TechnologyPennsylvania State UniversityState CollegeUSA

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