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Take the Power Back: Screen-Based Personal Moderation Against Hate Speech on Instagram

Anna Ricarda Luther, Hendrik Heuer, Stephanie Geise, Sebastian Haunss, Andreas Breiter

TL;DR

A three-wave Delphi study with 40 activists who experienced hate speech combined quantitative ratings and rankings with open questions about required features to address gaps in personal moderation on social media.

Abstract

Hate speech remains a pressing challenge on social media, where platform moderation often fails to protect targeted users. Personal moderation tools that let users decide how content is filtered can address some of these shortcomings. However, it remains an open question on which screens (e.g., the comments, the reels tab, or the home feed) users want personal moderation and which features they value most. To address these gaps, we conducted a three-wave Delphi study with 40 activists who experienced hate speech. We combined quantitative ratings and rankings with open questions about required features. Participants prioritized personal moderation for conversational and algorithmically curated screens. They valued features allowing for reversibility and oversight across screens, while input-based, content-type specific, and highly automated features are more screen specific. We discuss the importance of personal moderation and offer user-centered design recommendations for personal moderation on Instagram.

Take the Power Back: Screen-Based Personal Moderation Against Hate Speech on Instagram

TL;DR

A three-wave Delphi study with 40 activists who experienced hate speech combined quantitative ratings and rankings with open questions about required features to address gaps in personal moderation on social media.

Abstract

Hate speech remains a pressing challenge on social media, where platform moderation often fails to protect targeted users. Personal moderation tools that let users decide how content is filtered can address some of these shortcomings. However, it remains an open question on which screens (e.g., the comments, the reels tab, or the home feed) users want personal moderation and which features they value most. To address these gaps, we conducted a three-wave Delphi study with 40 activists who experienced hate speech. We combined quantitative ratings and rankings with open questions about required features. Participants prioritized personal moderation for conversational and algorithmically curated screens. They valued features allowing for reversibility and oversight across screens, while input-based, content-type specific, and highly automated features are more screen specific. We discuss the importance of personal moderation and offer user-centered design recommendations for personal moderation on Instagram.
Paper Structure (38 sections, 2 figures, 16 tables)

This paper contains 38 sections, 2 figures, 16 tables.

Figures (2)

  • Figure 1: The distribution of importance ratings across all screens in Wave 3 among 40 activist social media users who experienced hate speech directed at them. The vertical red dashed line indicates the 50% mark, showing which ratings are shared by the majority of participants. The screens are sorted by the share of participants who rate it as important.
  • Figure 2: This figure shows the example images shown to the participants for the screens All Comments (\ref{['fig:AllComments']}), Comments Own Posts (\ref{['fig:owncomments']}), Reels Tab (\ref{['fig:reels']}), Home Feed (\ref{['fig:homefeed']}) and All DMs (\ref{['fig:alldms']}). On top, the five highest-ranked personal moderation features for the respective screens are displayed, arranged according to the participant ranking.