Take the Power Back: Screen-Based Personal Moderation Against Hate Speech on InstagramHate 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.2026ALAnna Ricarda Luther et al.ifibOnline Harassment & Counter-ToolsSocial Platform Design & User BehaviorParticipatory DesignCHI
They Think AI Can Do More Than It Actually Can: Practices, Challenges, & Opportunities of AI-Supported Reporting In Local JournalismDeclining newspaper revenues prompt local newsrooms to adopt automation to maintain efficiency and keep the community informed. However, current research provides a limited understanding of how local journalists work with digital data and which newsroom processes would benefit most from AI-supported (data) reporting. To bridge this gap, we conducted 21 semi-structured interviews with local journalists in Germany. Our study investigates how local journalists use data and AI (RQ1); the challenges they encounter when interacting with data and AI (RQ2); and the self-perceived opportunities of AI-supported reporting systems through the lens of discursive design (RQ3). Our findings reveal that local journalists do not fully leverage AI's potential to support data-related work. Despite local journalists’ limited awareness of AI's capabilities, they are willing to use it to process data and discover stories. Finally, we provide recommendations for improving AI-supported reporting in the context of local news, grounded in the journalists’ socio-technical perspective and their imagined AI future capabilities.2026BCBesjon Cifliku et al.Center for Advanced Internet Studies (CAIS)Explainable AI (XAI)AI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityCHI
Reflecting on 1,000 Social Media Journeys: Generational Patterns in Platform TransitionSocial media has billions of users, but we still do not fully understand why users prefer one platform over another. Establishing new platforms among already popular competitors is difficult. Prior research has richly documented people's experiences within individual platforms, yet situating those experiences within the entirety of a user's social media experience remains challenging. What platforms have people used, and why have they transitioned between them? We collected data from a quota-based sample of 1,000 U.S. participants. We introduce the concept of \emph{Social Media Journeys} to study the entirety of their social media experiences systematically. We identify push and pull factors across the social media landscape. We also show how different generations adopted social media platforms based on personal needs. With this work, we advance HCI by moving towards holistic perspectives when discussing social media technology, offering new insights for platform design, governance, and regulation.2026ASArtur Solomonik et al.Center for Advanced Internet StudiesSocial Platform Design & User BehaviorContent Moderation & Platform GovernanceActivism & Political ParticipationCHI
A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMsLarge Language Models (LLMs) are increasingly used for mental health support, yet little is known about how people with mental health challenges engage with them, how they evaluate their usefulness, and what design opportunities they envision. We conducted 20 semi-structured interviews with people in the UK who live with mental health conditions and have used LLMs for mental health support. Through reflexive thematic analysis, we found that participants engaged with LLMs in conditional and situational ways: for immediacy, the desire for non-judgement, self-paced disclosure, cognitive reframing, and relational engagement. Simultaneously, participants articulated clear boundaries informed by prior therapeutic experience: LLMs were effective for mild-to-moderate distress but inadequate for crises, trauma, and complex social-emotional situations. We contribute empirical insights into the lived use of LLMs for mental health, highlight boundary-setting as central to their safe role, and propose design and governance directions for embedding them responsibly within care ecosystem.2026APAditya Kumar Purohit et al.Center for Advanced Internet Studies (CAIS)Human-LLM CollaborationMental Health Apps & Online Support CommunitiesMid-Air Haptics (Ultrasonic)CHI
When Handwriting Goes Social: Creativity, Anonymity, and Communication in Graphonymous Online SpacesWhile most digital communication platforms rely on text, relatively little research has examined how users engage through handwriting and drawing in anonymous, collaborative environments. We introduce Graphonymous Interaction, a form of communication where users interact anonymously via handwriting and drawing. Our study analyzed over 600 canvas pages from the Graphonymous Online Space (GOS) CollaNote and conducted interviews with 20 users. Additionally, we examined 70 minutes of real-time GOS sessions using Conversation Analysis and Multimodal Discourse Analysis. Findings reveal that Graphonymous Interaction fosters artistic expression, intellectual engagement, sharing and supporting, and social connection. Notably, anonymity coexisted with moments of recognition through graphological identification. Distinct conversational strategies also emerged, which allow smoother exchanges and fewer conversational repairs compared to text-based communication. This study contributes to understanding Graphonymous Interaction and Online Spaces, offering insights into designing platforms that support creative and socially engaging forms of communication beyond text.2026APAditya Kumar Purohit et al.Center for Advanced Internet Studies (CAIS)Tangible User Interface DesignPhysical-Digital Hybrid InteractionCollaborative Writing ToolsCHI
The Phase Model of Misinformation InterventionsMisinformation is a challenging problem. This paper provides the first systematic interdisciplinary investigation of technical and non-technical interventions against misinformation. It combines interviews and a survey to understand which interventions are accepted across academic disciplines and approved by misinformation experts. Four interventions are supported by more than two in three misinformation experts: promoting media literacy, education in schools and universities, finding information about claims, and finding sources for claims. The most controversial intervention is deleting misinformation. We discuss the potentials and risks of all interventions. Education-based interventions are perceived as the most helpful by misinformation experts. Interventions focused on providing evidence are also widely perceived as helpful. We discuss them as scalable and always available interventions that empower users to independently identify misinformation. We also introduce the Phase Model of Misinformation Interventions that helps practitioners make informed decisions about which interventions to focus on and how to best combine interventions.2025HHHendrik HeuerMitigating MisinformationCSCW
Social Media for Activists: Reimagining Safety, Content Presentation, and WorkflowsSocial media is central to activists, who use it internally for coordination and externally to reach supporters and the public. To date, the HCI community has not explored activists' perspectives on future social media platforms. In interviews with 14 activists from an environmental and a queer-feminist movement in Germany, we identify activists' needs and feature requests for future social media platforms. The key finding is that on- and offline safety is their main need. Based on this, we make concrete proposals to improve safety measures. Increased control over content presentation and tools to streamline activist workflows are also central to activists. We make concrete design and research recommendations on how social media platforms and the HCI community can contribute to improved safety and content presentation, and how activists themselves can reduce their workload.2025ALAnna Ricarda Luther et al.Institute for Information Management Bremen GmbH; University of BremenSocial Platform Design & User BehaviorActivism & Political ParticipationCHI
Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social MediaInfinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.2025LMLuca-Maxim Meinhardt et al.Institute of Media Informatics, Ulm UniversityNotification & Interruption ManagementCHI
Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic VariationsCommercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing black-box NLP systems. Using the framework, we systematically evaluate five widely used commercial content moderation APIs. Analyzing five million queries based on four datasets, we find that APIs frequently rely on group identity terms, such as ``black'', to predict hate speech. While OpenAI's and Amazon's services perform slightly better, all providers under-moderate implicit hate speech, which uses codified messages, especially against LGBTQIA+ individuals. Simultaneously, they over-moderate counter-speech, reclaimed slurs and content related to Black, LGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer better guidance on API implementation and threshold setting and more transparency on their APIs' limitations. \noindent \textit{\textbf{Warning}: This paper contains offensive and hateful terms and concepts. We have chosen to reproduce these terms for reasons of transparency.}2025DHDavid Hartmann et al.Weizenbaum Institute Berlin, Data, Algorithmic Systems and Ethics; Technical University Berlin, Internet and SocietyAI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityDark Patterns RecognitionCHI