Drawing from justice theories to support targets of online harassment

Most content moderation approaches in the United States rely on criminal justice models that sanction offenders via content removal or user bans. However, these models write the online harassment targets out of the justice-seeking process. Via an online survey with US participants (N = 573), this research draws from justice theories to investigate approaches for supporting targets of online harassment. We uncover preferences for banning offenders, removing content, and apologies, but aversion to mediation and adjusting targets’ audiences. Preferences vary by identities (e.g. transgender participants on average find more exposure to be undesirable; American Indian or Alaska Native participants on average find payment to be unfair) and by social media behaviors (e.g. Instagram users report payment as just and fair). Our results suggest that a one-size-fits-all approach will fail some users while privileging others. We propose a broader theoretical and empirical landscape for supporting online harassment targets.This material is based upon work supported by the National Science Foundation under Grant No. 1763297.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154685/1/SchoenebeckDrawingFromJustice.pdfDescription of SchoenebeckDrawingFromJustice.pdf : Main articl