Content Moderation and Hate Speech on Alternative Platforms: A Case Study of BitChuteFrustration with mainstream social media platforms and their content moderation decisions have prompted many users to search for “anti-censorship” alternatives, such as those in alt-tech, which may be used to share extreme or hateful content. The tension in alt-tech between limiting perceived censorship and reducing hate speech means that content moderation policies are essential but also controversial. Nevertheless, alt-tech content moderation policies are understudied. To address this research gap, we leverage quasi-experimental design to measure the impact of an “incitement to hatred” policy change using 5.2 million comments and the metadata of 800 thousand videos from the alt-tech platform BitChute. We uncover evidence for a “backlash effect,” finding that after the implementation of the policy, hate speech increased significantly for comments and video metadata. This study contributes to the literature on content moderation policies in a challenging context where users may not be receptive to perceived impositions.2025JEJacob Erickson et al.Hate SpeechCSCW
Typing Haptically: Towards Enabling Non-auditory Smartphone Text Entry with Haptic Feedback for Blind and Low Vision UsersText entry on smartphones remains challenging for Blind and Low Vision (BLV) users, particularly in environments where audio feedback is impractical due to noise, privacy, or social stigma. We present TypeHap, a new system that enables BLV users to type confidently on smartphones using only haptic feedback without relying on audio. Through formative interviews (N=20), we identified the key user needs and iteratively designed a compact, attachable system combining phoneme-based haptic cues delivered through piezo actuators embedded on both sides of the smartphone and a tactile overlay on a touchscreen for differentiating rows in a keyboard. In a four-day study (N=11), BLV participants trained with TypeHap achieved text entry speeds and accuracies comparable to typing with conventional audio feedback. Participants described TypeHap as liberating in public, noisy, and private contexts where audio feedback falls short. Our findings highlight haptic feedback as a promising alternative to audio-based interaction for enabling more private, accessible smartphone use of BLV users in diverse everyday contexts.2025JYJisu Yim et al.Vibrotactile Feedback & Skin StimulationVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)UIST
The Image of the Metaverse: A Plurality of Narratives for Immersive RealitiesThis pictorial explores the challenges and opportunities of creating meaningful virtual spaces in the metaverse. Drawing inspiration from Kevin Lynch’s principles of urban imageability, the authors present a series of narrative explorations and associated graphics that reimagine how space, time, and social interaction might function in virtual environments. The work identifies key differences between physical and virtual architectures, including perceptual fungibility, non-linear spatial relationships, and collective emergence. Through detailed narratives organized around themes of arrival, boundary, navigation, connection, and memory, the pictorial proposes new organizing principles for metaverse design that embrace discontinuity, fluid boundaries, and social physics rather than attempting to replicate physical space. The work contributes theoretical frameworks and methodological insights for developing more imageable, engaging, and coherent virtual worlds.2025JHJihae Han et al.AR Navigation & Context AwarenessImmersion & Presence ResearchInteractive Narrative & Immersive StorytellingDIS
Simulating Cooperative Prosocial Behavior with Multi-Agent LLMs: Evidence and Mechanisms for AI Agents to Inform Policy DecisionsHuman prosocial cooperation is essential for our collective health, education, and welfare. However, designing social systems to maintain or incentivize prosocial behavior is challenging because people can act selfishly to maximize personal gain. This complex and unpredictable aspect of human behavior makes it difficult for policymakers to foresee the implications of their designs. Recently, multi-agent LLM systems have shown remarkable capabilities in simulating human-like behavior, and replicating some human lab experiments. This paper studies how well multi-agent systems can simulate prosocial human behavior, such as that seen in the public goods game (PGG), and whether multi-agent systems can exhibit “unbounded actions” seen outside the lab in real world scenarios. We find that multi-agent LLM systems successfully replicate human behavior from lab experiments of the public goods game with three experimental treatments - priming, transparency, and varying endowments. Beyond replicating existing experiments, we find that multi-agent LLM systems can replicate the expected human behavior when combining experimental treatments, even if no previous study combined those specific treatments. Lastly, we find that multi-agent systems can exhibit a rich set of unbounded actions that people do in the real world outside of the lab – such as collaborating and even cheating. In sum, these studies are steps towards a future where LLMs can be used to inform policy decisions that encourage people to act in a prosocial manner.2025KSKarthik Sreedhar et al.Human-LLM CollaborationAI-Assisted Decision-Making & AutomationAlgorithmic Fairness & BiasIUI
Shape-Kit: A Design Toolkit for Crafting On-Body Expressive HapticsDriven by the vision of everyday haptics, the HCI community is advocating for “design touch first” and investigating “how to touch well.” However, a gap remains between the exploratory nature of haptic design and technical reproducibility. We present Shape-Kit, a hybrid design toolkit embodying our “crafting haptics” metaphor, where hand touch is transduced into dynamic pin-based sensations that can be freely explored across the body. An ad-hoc tracking module captures and digitizes these patterns. Our study with 14 designers and artists demonstrates how Shape-Kit facilitates sensorial exploration for expressive haptic design. We analyze how designers collaboratively ideate, prototype, iterate, and compose touch experiences and show the subtlety and richness of touch that can be achieved through diverse crafting methods with Shape-Kit. Reflecting on the findings, our work contributes key insights into haptic toolkit design and touch design practices centered on the “crafting haptics” metaphor. We discuss in-depth how Shape-Kit’s simplicity, though remaining constrained, enables focused crafting for deeper exploration, while its collaborative nature fosters shared sense-making of touch experiences.2025RZRan Zhou et al.University of Chicago; KTH Royal Institute of TechnologyHaptic WearablesShape-Changing Interfaces & Soft Robotic MaterialsCHI
FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic SettingsDepression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures -- all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.2024MIMohammad Rahul Islam et al.Human Pose & Activity RecognitionMental Health Apps & Online Support CommunitiesSleep & Stress MonitoringMobileHCI
ReelFramer: Human-AI Co-Creation for News-to-Video TranslationShort videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels---short videos conveying news---but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.2024SWSitong Wang et al.Columbia UniversityAI-Assisted Creative WritingVideo Production & EditingCHI
Affective Design: The Influence of Facebook Reactions on the Emotional Expression of the 114th US CongressPolitical communication is critical for democracy, but polarized emotions in communication may make careful deliberation difficult. Much of modern political communication occurs on social media, which may exacerbate these challenges. This study examines how the design of social media features impact political communication. We examined how the introduction of Facebook Reactions influenced the posts of the 114th US Congress on the platform. We start by analyzing the emotional content of posts, finding that politicians generally increased their usage of negative emotions in their posts after the feature's launch. Further analysis showed that increased user engagement preceded the rise in negative emotions, suggesting that politicians were making adjustments based on user feedback. Our results show that the design features of social media can shape online political communication.2024JEJacob Erickson et al.Stevens Institute of TechnologySocial Platform Design & User BehaviorActivism & Political ParticipationAlgorithmic Fairness & BiasCHI
S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentIn public health and safety, precise detection of blood alcohol concentration (BAC) plays a critical role in implementing responsive interventions that can save lives. While previous research has primarily focused on computer-based or neuropsychological tests for BAC identification, the potential use of daily smartphone activities for BAC detection in real-life scenarios remains largely unexplored. Drawing inspiration from Instrumental Activities of Daily Living (I-ADL), our hypothesis suggests that Smartphone-based Activities of Daily Living (S-ADL) can serve as a viable method for identifying BAC. In our proof-of-concept study, we propose, design, and assess the feasibility of using S-ADLs to detect BAC in a scenario-based controlled laboratory experiment involving 40 young adults. In this study, we identify key S-ADL metrics, such as delayed texting in SMS, site searching, and finance management, that significantly contribute to BAC detection (with an AUC-ROC and accuracy of 81%). We further discuss potential real-life applications of the proposed BAC model.2024HLHansoo Lee et al.Korea Advanced Institute of Science and TechnologyHuman Pose & Activity RecognitionMental Health Apps & Online Support CommunitiesBiosensors & Physiological MonitoringCHI
AngleKindling: Supporting Journalistic Angle Ideation with Large Language ModelsNews media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.2023SPSavvas Petridis et al.Columbia UniversityHuman-LLM CollaborationAI-Assisted Creative WritingUser Research Methods (Interviews, Surveys, Observation)CHI
Crowdsourcing More Effective Initializations for Single-Target Trackers Through Automatic Re-queryingIn single-target video object tracking, an initial bounding box is drawn around a target object and propagated through a video. When this bounding box is provided by a careful human expert, it is expected to yield strong overall tracking performance that can be mimicked at scale by novice crowd workers with the help of advanced quality control methods. However, we show through an investigation of 900 crowdsourced initializations that such quality control strategies are inadequate for this task in two major ways: first, the high level of redundancy in these methods (e.g., averaging multiple responses to reduce error) is unnecessary, as 23\% of crowdsourced initializations perform just as well as the gold-standard initialization. Second, even nearly perfect initializations can lead to degraded long-term performance due to the complexity of object tracking. Considering these findings, we evaluate novel approaches for automatically selecting bounding boxes to re-query, and introduce \textit{Smart Replacement}, an efficient method that decides whether to use the crowdsourced replacement initialization.2021SLStephan J Lemmer et al.University of MichiganInteractive Data VisualizationCrowdsourcing Task Design & Quality ControlComputational Methods in HCICHI
The Roles Bots Play in Wikipedia Bots are playing an increasingly important role in the creation of knowledge in Wikipedia. In many cases, editors and bots form tightly knit teams. Humans develop bots, argue for their approval, and maintain them, performing tasks such as monitoring activity, merging similar bots, splitting complex bots, and turning off malfunctioning bots. Yet this is not the entire picture. Bots are designed to perform certain functions and can acquire new functionality over time. They play particular roles in the editing process. Understanding these roles is an important step towards understanding the ecosystem, and designing better bots and interfaces between bots and humans. This is important for understanding Wikipedia along with other kinds of work in which autonomous machines affect tasks performed by humans. In this study, we use unsupervised learning to build a nine category taxonomy of bots based on their functions in English Wikipedia. We then build a multi-class classifier to classify 1,601 bots based on labeled data. We discuss different bot activities, including their edit frequency, their working spaces, and their software evolution. We use a model to investigate how bots playing certain roles will have differential effects on human editors. In particular, we build on previous research on newcomers by studying the relationship between the roles bots play, the interactions they have with newcomers, and the ensuing survival rate of the newcomers.2019LZLei (Nico) Zheng et al.Wikis and WikipediaCSCW