Disabled people on social media often experience ableist hate and microaggressions. Prior work has shown that platform moderation often fails to remove ableist hate, leaving disabled users exposed to harmful content. This paper examines how personalized moderation can safeguard users from viewing ableist comments. During interviews and focus groups with 23 disabled social media users, we presented design probes to elicit perceptions on configuring their filters of ableist speech (e.g., intensity of ableism and types of ableism) and customizing the presentation of the ableist speech to mitigate the harm (e.g., AI rephrasing the comment and content warnings). We found that participants preferred configuring their filters through types of ableist speech and favored content warnings. We surface participants’ distrust in AI-based moderation, skepticism in AI’s accuracy, and varied tolerances in viewing ableist hate. Finally, we share design recommendations to support users’ agency, mitigate harm from hate, and promote safety.
https://dl.acm.org/doi/10.1145/3706598.3713997
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