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Computer Science > Social and Information Networks

arXiv:1902.03093 (cs)
[Submitted on 31 Jan 2019]

Title:A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter

Authors:Laure Delisle, Alfredo Kalaitzis, Krzysztof Majewski, Archy de Berker, Milena Marin, Julien Cornebise
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Abstract:We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter. On a technical front, we carefully curate an unbiased yet low-variance dataset of labeled tweets, analyze it to account for the variability of abuse perception, and establish baselines, preparing it for release to community research efforts. On a social impact front, this study provides the technical backbone for a media campaign aimed at raising public and deciders' awareness and elevating the standards expected from social media companies.
Comments: Workshop on AI for Social Good, NeurIPS 2018
Subjects: Social and Information Networks (cs.SI); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
Cite as: arXiv:1902.03093 [cs.SI]
  (or arXiv:1902.03093v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.1902.03093

Submission history

From: Alfredo Kalaitzis [view email]
[v1] Thu, 31 Jan 2019 16:59:01 UTC (1,031 KB)
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