Fairness across network positions in cyberbullying detection algorithms

Vivek K. Singh, Connor Hofenbitzer

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

Abstract

Cyberbullying, which often has a deeply negative impact on the victim, has grown as a serious issue in online social networks. Recently, researchers have created automated machine learning algorithms to detect Cyberbullying using social and textual features. However, the very algorithms that are intended to fight off one threat (cyberbullying) may inadvertently be falling prey to another important threat (bias of the automatic detection algorithms). This is exacerbated by the fact that while the current literature on algorithmic fairness has multiple empirical results, metrics, and algorithms for countering bias across immediately observable demographic characteristics (e.g. age, race, gender), there have been no efforts at empirically quantifying the variation in algorithmic performance based on the network role or position of individuals. We audit an existing cyberbullying algorithm using Twitter data for disparity in detection performance based on the network centrality of the potential victim and then demonstrate how this disparity can be countered using an Equalized Odds post-processing technique. The results pave the way for more accurate and fair cyberbullying detection algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
EditorsFrancesca Spezzano, Wei Chen, Xiaokui Xiao
PublisherAssociation for Computing Machinery, Inc
Pages557-559
Number of pages3
ISBN (Electronic)9781450368681
DOIs
StatePublished - Aug 27 2019
Event11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duration: Aug 27 2019Aug 30 2019

Publication series

NameProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Conference

Conference11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
CountryCanada
CityVancouver
Period8/27/198/30/19

All Science Journal Classification (ASJC) codes

  • Communication
  • Computer Networks and Communications
  • Information Systems and Management
  • Sociology and Political Science

Keywords

  • Algorithmic fairness
  • Cyberbullying
  • Network position

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  • Cite this

    Singh, V. K., & Hofenbitzer, C. (2019). Fairness across network positions in cyberbullying detection algorithms. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 557-559). (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342949