CHCI Research Featured at CSCW 2024

The faculty and students of CHCI have made substantial contributions to the 27th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), which will occur in San José, Costa Rica, on November 9-13, 2024.

CSCW is the premier venue for research in the design and use of technologies that affect groups, organizations, communities, and networks. Bringing together top researchers and practitioners, CSCW explores the technical, social, material, and theoretical challenges of designing technology to support collaborative work and life activities.

Paper Presentations

(CHCI members in bold)

From awareness to action: Exploring end-user empowerment interventions for dark patterns in UX 

[Best Paper Award]

Authors: Yuwen Lu, Chao Zhang, Yuewen Yang, Yaxing Yao, Toby Li

Abstract: The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer’s perspective on education and advocacy for ethical designs; and (2) the policymaker’s perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.

Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media 

[DEI Recognition Award]

Authors: Uma Sushmitha Gunturi (Alumni), Anisha Kumar (Alumni), Xiaohan Ding, Eugenia H. Rho

Abstract: In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.

OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence Investigations

Authors: Anirban Mukhopadhyay, Sukrit Venkatagiri (Alumni), Kurt Luther

Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex nature of tasks, however, means there is a pressing need to scale and speed up OSINT investigations. Expert-led crowdsourcing approaches show promise, but tend to either focus on narrow tasks or domains, or require resource-intense, long-term relationships between expert investigators and crowds. We address this gap by providing a flexible framework that enables investigators across domains to enlist crowdsourced support for discovery and verification of OSINT. We use a design-based research (DBR) approach to develop OSINT Research Studios (ORS), a sociotechnical system in which novice crowds are trained to support professional investigators with complex OSINT investigations. Through our qualitative evaluation, we found that ORS facilitates ethical and effective OSINT investigations across multiple domains. We also discuss broader implications of expert--crowd collaboration and opportunities for future work.

Understanding Multi-user, Handheld Mixed Reality for Group-based MR Games

Authors: Carlos Augusto Bautista Isaza, Daniel Enriquez (Alumni), Hayoun Moon, Myounghoon Jeon, Sang Won Lee

Abstract: Research has identified applications of handheld-based VR, which utilizes handheld displays or mobile devices, for developing systems that involve users in mixed reality (MR) without the need for head-worn displays (HWDs). Such systems can potentially accommodate large groups of users participating in MR. However, we lack an understanding of how group sizes and interaction methods affect the user experience. In this paper, we aim to advance our understanding of handheld-based MR in the context of multiplayer, co-located games. We conducted a study (N = 38) to understand how user experiences vary by group size (2, 4, and 8) and interaction method (proximity-based or pointing-based). For our experiment, we implemented a multiuser experience for up to ten users. We found that proximity-based interaction that encouraged dynamic movement positively affected social presence and physical/temporal workload. In bigger group settings, participants felt less challenged and less positive. Individuals had varying preferences for group size and interaction type. The findings of the study will advance our understanding of the design space for handheld-based MR in terms of group sizes and interaction schemes. To make our contributions explicit, we conclude our paper with design implications that can inform user experience design in handheld-based mixed reality contexts. Watch the user study video.

Understanding the Relationship Between Social Identity and Self-Expression Through Animated Gifs on Social Media

Authors: Marx Wang (Alumni), Md Momen Bhuiyan (Alumni), Eugenia Rho, Kurt Luther, Sang Won Lee

Abstract: GIFs afford a high degree of personalization, as they are often created from popular movie and video clips with diverse and realistic characters, each expressing a nuanced emotional state through a combination of characters’ own unique bodily gestures and distinctive visual backgrounds. These properties of high personalization and embodiment provide a unique window for exploring how individuals represent and express themselves on social media through the lens of the GIFs they use. In this study, we explore how Twitter users express their gender and racial identities through characters in GIFs. We conducted a behavioral study (n = 398) to simulate a series of tweeting and GIF-picking scenarios. We annotated the gender and race identities of GIF characters, and we found that gender and race identities have significant impacts on users’ GIF choices: men chose more gender-matching GIFs than women, and White participants chose more race-matching GIFs than Black participants. We also found that users’ prior familiarity with the source of a GIF and perceptions about the composition of the audience (viz., having a matching identity) have significant effects on whether a user will choose race- and gender-matching GIFs. This work has implications for practitioners supporting personalized social identity construction and impression management mechanisms online.

Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities

Authors: Andreea Sistrunk, Nathan Self, Subhodip Biswas, Kurt Luther, Nervo Verdezoto, Naren Ramakrishnan

Abstract: Public deliberations are often a staple ingredient in community decision-making. However, traditional, timeconstrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders’ values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.

Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

Authors: Md Momen Bhuiyan (Alumni), Donghan Hu, Andrew Jelson, Tanushree Mitra, Sang Won Lee

Abstract: Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendations, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowds have better knowledge than search systems. Examining users’ queries and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifact recommendations based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Users' Perceptions of Online Child Abuse Detection Mechanisms

Elmira Deldari, Parth Thakkar, Yaxing Yao

Abstract: Child sexual exploitation and abuse (CSEA) online has become a major safety issue for children to access the Internet. To combat CSEA, electronics services providers (ESP) have implemented various mechanisms to detect child sexual abuse materials (CSAM). However, these mechanisms, despite their capability to prevent the mass distribution of CSAM online, may raise significant privacy concerns among general users. In this paper, we conducted a semi-structured interview study with 23 participants to understand their privacy perceptions of two types of online CSAM detection mechanisms. Our results suggested that users were concerned about the transparency of the detection process, inappropriate access to users' data, and unclear boundaries of such mechanisms. Our results also highlight that, even though the majority of participants choose to sacrifice their privacy for societal benefits, they still have privacy concerns that need to be addressed. We discuss the design and policy implications for ESP to improve users' awareness of the data practices of these mechanisms, alleviate users' privacy concerns, and increase societal benefits.

Understanding Chinese Internet Users' Perceptions of, and Online Platforms' Compliance with, the Personal Information Protection Law (PIPL)

Mo Zhou, Zhiyan Qu, Jinhan Wan, Bo Wen, Yaxing Yao, Zhicong Lu

Abstract: The Personal Information Protection Law (PIPL) was implemented in November 2021 to safeguard the personal information rights and interests of Internet users in China. However, the impact and existing shortcomings of the PIPL remain unclear, carrying significant implications for policymakers.

This study examined privacy policies on 13 online platforms before and after the PIPL. Concurrently, it conducted semi-structured interviews with 30 Chinese Internet users to assess their perceptions of the PIPL. Users were also given tasks to identify non-compliance within the platforms, assessing their ability to address related privacy concerns effectively.

The research revealed various instances of non-compliance in post-PIPL privacy policies, especially concerning inadequate risk assessments for sensitive data. Although users identified some non-compliant activities like app eavesdropping, issues related to individual consent proved challenging. Surprisingly, over half of the interviewees believed that the government could access their personal data without explicit consent.

Our findings and implications can be valuable for lawmakers, online platforms, users, and future researchers seeking to enhance personal privacy practices both in China and globally.

Poster Presentations

(CHCI members in bold)

Technology Use in the Black Church: Perspectives of Black Church Leaders Preliminary Findings

Authors: Gabriella Thompson, Nissi Otoo, Jaden Christopher Fisher, Irene Sibi, Angela D. R. Smith, Ihudiya Finda Ogbonnaya-Ogburu

Abstract: Historically, the Black Church has played a pivotal role in civic engagement and social justice, and continues to do so today. Yet, few researchers have explored how decisions around technology use are made in the church. To address this gap, we conducted semi-structured interviews with five Black Church leaders to understand how church leaders interact with digital technologies, both in general and specifically with the communities that they serve.  We found that while Black Church leaders are eager to engage with technology, most of the engagement with outside communities is through in-person contact; opportunities to give online have a financial penalty in comparison to traditional methods of tithing and donating; lastly, technology use within outreach and ministries is highly dependent by ministry leaders -- many who volunteer their time. We contribute to research that focuses on technology use in religious organizations and community engagement of community-based organizations.

Designing Technology to Support the Hospital Classroom: Preliminary Findings

Authors: Nadra Rasberry, Joshua Essandoh, Ethan Do, Ihudiya Finda Ogbonnaya-Ogburu

Abstract: Hospital teachers are state-employed educators who provide K-12 instruction to children in the hospital. We conducted research to understand how technology is used in hospital classrooms, an area which has been relatively underexplored. We conducted semi-structured interviews with five hospital teachers to understand their experience of using technology in and outside the classroom. Our findings revealed that hospital teachers often rely on older curricula given the changing education atmosphere; learning is often assessed through in-classroom observations of mastery; and technology and internet use by students is often restricted, which may inhibit opportunities to use AI and other technical resources in the classroom. We contribute a deeper understanding of technology use in the hospital classroom.

Evaluation of Interactive Demonstration in Voice-assisted Counting for Young Children

List of authors: Sulakna Karunaratna, Daniel Vargas-Diaz (Alumni), Jisun Kim, Jenny Wang, Koeun Choi, Sang Won Lee

Abstract: In recent years, the number of AI voice agent applications designed to help young children learn math has increased. However, the impact of interactivity within these applications on children's learning and engagement remains unexplored. While current apps may employ various levels of interactions, such as visual, haptic, sound, and animation, the efficacy of these interactions in facilitating children's learning remains uncertain.  This research investigates how varying levels of interactivity in touch-based interfaces, combined with an AI voice agent, affect the learning of counting skills in children aged 2 to 4 years. We examine three conditions: baseline (no demonstration), animated demonstration, and interactive demonstration. By examining how these different levels of interactivity influence children's engagement with math apps, this study seeks to enhance our understanding of effective design strategies for educational technology targeting early childhood education. The findings of this research hold the potential to inform the development of interfaces for math games that leverage both touch-based interactions and AI voice assistants to support young children's learning of foundational mathematical concepts.