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Ex Machina: Personal Attacks Seen at Scale

Published: 03 April 2017 Publication History

Abstract

The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users.

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cover image ACM Other conferences
WWW '17: Proceedings of the 26th International Conference on World Wide Web
April 2017
1678 pages
ISBN:9781450349130

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Author Tags

  1. online discussions
  2. online harassment
  3. wikipedia

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WWW '17
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  • IW3C2

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2025)Utilizing Fractional Order Epidemiological Model to Understand High and Moderate Toxicity Spread on Social Media PlatformsSocial Networks Analysis and Mining10.1007/978-3-031-78538-2_26(298-308)Online publication date: 25-Jan-2025
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