2019/01/31 by Laure Delisle, Freddie Kalaitzis, Delisle, Laure +9 · 5 citations
Computer Science · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Misinformation and Its Impacts #Social Media and Politics #Social and Information Networks (cs.SI) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1902.03093
openalex publication_date 2019/01/31 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
We report the first, to the best of our knowledge, hand-in-hand collaboration\nbetween human rights activists and machine learners, leveraging crowd-sourcing\nto study online abuse against women on Twitter. On a technical front, we\ncarefully curate an unbiased yet low-variance dataset of labeled tweets,\nanalyze it to account for the variability of abuse perception, and establish\nbaselines, preparing it for release to community research efforts. On a social\nimpact front, this study provides the technical backbone for a media campaign\naimed at raising public and deciders' awareness and elevating the standards\nexpected from social media companies.\n