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1. Women's Perspectives on Harm and Justice after Online Harassment

   https://doi.org/10.1145/3555775  

   Im, Jane; Schoenebeck, Sarita; Iriarte, Marilyn; Grill, Gabriel; Wilkinson, Daricia; Batool, Amna; Alharbi, Rahaf; Funwie, Audrey; Gankhuu, Tergel; Gilbert, Eric; et al (November 2022, Proceedings of the ACM on Human-Computer Interaction)

   Social media platforms aspire to create online experiences where users can participate safely and equitably. However, women around the world experience widespread online harassment, including insults, stalking, aggression, threats, and non-consensual sharing of sexual photos. This article describes women's perceptions of harm associated with online harassment and preferred platform responses to that harm. We conducted a survey in 14 geographic regions around the world (N = 3,993), focusing on regions whose perspectives have been insufficiently elevated in social media governance decisions (e.g. Mongolia, Cameroon). Results show that, on average, women perceive greater harm associated with online harassment than men, especially for non-consensual image sharing. Women also prefer most platform responses compared to men, especially removing content and banning users; however, women are less favorable towards payment as a response. Addressing global gender-based violence online requires understanding how women experience online harms and how they wish for it to be addressed. This is especially important given that the people who build and govern technology are not typically those who are most likely to experience online harms. 

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2. User Preferences for Interdependent Privacy Preservation Strategies in Social Media

   https://doi.org/10.1145/3610062  

   Necaise, Aaron; Tanni, Tangila Islam; Williams, Aneka; Solihin, Yan; Kapadia, Apu; Amon, Mary Jean (September 2023, Proceedings of the ACM on Human-Computer Interaction)

   Interdependent privacy (IDP) violations occur when users share personal information about others without permission, resulting in potential embarrassment, reputation loss, or harassment. There are several strategies that can be applied to protect IDP, but little is known regarding how social media users perceive IDP threats or how they prefer to respond to them. We utilized a mixed-method approach with a replication study to examine user beliefs about various government-, platform-, and user-level strategies for managing IDP violations. Participants reported that IDP represented a 'serious' online threat, and identified themselves as primarily responsible for responding to violations. IDP strategies that felt more familiar and provided greater perceived control over violations (e.g., flagging, blocking, unfriending) were rated as more effective than platform or government driven interventions. Furthermore, we found users were more willing to share on social media if they perceived their interactions as protected. Findings are discussed in relation to control paradox theory. 

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3. Health-related Misinformation Harm during the COVID- 19 Pandemic: An Investigation of Non-comparative and Comparative Harm Perceptions

   Tran, Thi; Valecha, Rohit; Rao, Raghav (February 2022, AIS Transactions on Human-Computer Interaction)

   Nah, Fiona (Ed.)

   Misinformation about the coronavirus disease of 2019 (COVID-19) health crisis has been widespread on social media and caused various types of harms in society. While some researchers have investigated the way in which people perceive misinformation harm in crises, little research has systematically examined harms from health-related misinformation. In order to address this gap, we focus on non-comparative and comparative harm perceptions of the affected community in the COVID-19 pandemic context. We examine non-comparative harms (which component harms and contextual harms reflect) and comparative harms (which counter-contextual harms reflect) in order to understand harm perceptions. We also investigate how harm perception varies based on COVID-19 victimization experience. We used a professional survey company named Cint to collect data using a scenario-based survey with 343 participants. We extract various findings such as how contextual features shape perceived harms and reveal the scenarios in which COVID-19 victims perceive higher contextual harms but lower counter-contextual harms. We also examine how corrective actions of social media shape harm perceptions. 

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4. Addressing Interpersonal Harm in Online Gaming Communities: The Opportunities and Challenges for a Restorative Justice Approach

   https://doi.org/10.1145/3603625  

   Xiao, Sijia; Jhaver, Shagun; Salehi, Niloufar (September 2023, ACM Transactions on Computer-Human Interaction)

   Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges. 

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5. Generative AI and Perceptual Harms: Who's Suspected of using LLMs?

   Kadoma, Kowe; Metaxa, Danaë; Naaman, Mor (October 2024, arXiv)

   Large language models (LLMs) are increasingly integrated into a variety of writing tasks. While these tools can help people by generating ideas or producing higher quality work, like many other AI tools they may risk causing a variety of harms, disproportionately burdening historically marginalized groups. In this work, we introduce and evaluate perceptual harm, a term for the harm caused to users when others perceive or suspect them of using AI. We examined perceptual harms in three online experiments, each of which entailed human participants evaluating the profiles for fictional freelance writers. We asked participants whether they suspected the freelancers of using AI, the quality of their writing, and whether they should be hired. We found some support for perceptual harms against for certain demographic groups, but that perceptions of AI use negatively impacted writing evaluations and hiring outcomes across the board. 

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