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Online abuse is becoming an increasingly prevalent issue in modern-daysociety, with 41 percent of Americans having experienced online harassment insome capacity in 2021. People who identify as women, in particular, can besubjected to a wide range of abusive behavior online, with gender-specificexperiences cited broadly in recent literature across fields such as blogging,politics, and journalism. In response to this rise in abusive content,platforms have been found to largely employ "individualistic moderation"approaches, aiming to protect users from harmful content through the screeningand management of singular interactions or accounts. Yet, previous workperformed by the author of this paper has shown that in the cases of women inparticular, these approaches can often be ineffective; failing to protect usersfrom multi-dimensional abuse spanning prolonged time periods, differentplatforms, and varying interaction types. In recognition of its increasingcomplexity, platforms are beginning to outsource content moderation to users ina new and decentralized approach. The goal of this research is to examine thefeasibility of using multidimensional abuse indicators in a Twitter-basedmoderation algorithm aiming to protect women from female-targeted online abuse.This research outlines three indicators of multidimensional abuse, explores howthese indicators can be extracted as features from Twitter data, and proposes atechnical framework for deploying an end-to-end moderation algorithm usingthese features.

The Feasibility of Algorithmic Detection and Decentralised Moderation for Protecting Women from Online Abuse