CHCI@VT Research Showcased at ACM CSCW 2025

Behind the Counter: Exploring the Motivations and Barriers of Online Counterspeech Writing

Kaike Ping, Anisha Kumar, Xiahan Ding, Eugenia H Rho

Current research mainly explores the attributes and impact of online counterspeech, leaving a gap in understanding of who engages in online counterspeech or what motivates or deters users from participating. To investigate this, we surveyed 458 English-speaking U.S. participants, analyzing key motivations and barriers underlying online counterspeech engagement. We presented each participant with three hate speech examples from a set of 900, spanning race, gender, religion, sexual orientation, and disability, and requested counterspeech responses. Subsequent questions assessed their satisfaction, perceived difficulty, and the effectiveness of their counterspeech. Our findings show that having been a target of online hate is a key driver of frequent online counterspeech engagement. People differ in their motivations and barriers towards engaging in online counterspeech across different demographic groups. Younger individuals, women, those with higher education levels, and regular witnesses to online hate are more reluctant to engage in online counterspeech due to concerns around public exposure, retaliation, and third-party harassment. Varying motivation and barriers in counterspeech engagement also shape how individuals view their own self-authored counterspeech and the difficulty experienced writing it. Additionally, our work explores people’s willingness to use AI technologies like ChatGPT for counterspeech writing. Through this work we introduce a multi-item scale for understanding counterspeech motivation and barriers and a more nuanced understanding of the factors shaping online counterspeech engagement.

"Fewer Views If They Have TW.": Understanding Users' Perceptions of Trigger Warning and Content Warning on Social Media Platforms in the U.S.

Xinyi Zhang, Muskan Gupta, Emily Altland, Sang Won Lee

The prevalence of distressing content on social media raises concerns about users' mental well-being, prompting the use of trigger warnings (TW) and content warnings (CW). However, inconsistent presentation of TW/CW across platforms and the lack of standardized practices confuse users regarding these warnings. To better understand how users experienced and utilized these warnings, we conducted a semi-structured interview study with 15 social media users. Our findings reveal challenges across three key stakeholders: viewers, who need to decide whether to engage with warning-labeled content; posters, who struggle with whether and how to apply TW/CW to the content; and platforms, whose design features shape the visibility and usability of warnings. While users generally expressed positive attitudes toward warnings, their understanding of TW/CW usage was limited. Based on these insights, we reflected on the TW/CW mechanisms from multiple stakeholders' perspectives. Lastly, we further reflected on our findings and discussed the opportunities for social media platforms to enhance users' TW/CW experiences, fostering a more trauma-informed social media environment.

How Social Media Plays A Role in Stay-At-Home-Mom's Transition: A Case Study in China

Xinyi Zhang, Minzhu Zhao, Yaxing Yao, Zhicong Lu

In China, stay-at-home moms (SAHMs) often experience a hard time during their transitions, such as going back to school or restarting their careers. Yet, their experiences throughout this transition and their strategies to overcome potential challenges are rarely studied in the CSCW literature. In this study, we examined how Chinese SAHMs leveraged social media platforms to build resilience and the roles these platforms played as they re-engaged with society through interviews with 15 moms who have successfully completed their transitions and actively contributed related content on social media.

Perceiving and Countering Hate: The Role of Identity in Online Responses

Kaike Ping, James Hawdon, Eugenia H Rho

This study investigates how online counterspeech, defined as direct responses to harmful online content with the intention of dissuading the perpetrator from further engaging in such behavior, is influenced by the match between a target of the hate speech and a counterspeech writer's identity. Using a sample of 458 English-speaking adults who responded to online hate speech posts covering race, gender, religion, sexual orientation, and disability status, our research reveals that the match between a hate post’s topic and a counter-speaker’s identity (topic-identity match, or TIM) shapes perceptions of hatefulness and experiences with counterspeech writing. Specifically, TIM significantly increases the perceived hatefulness of posts related to race and sexual orientation. TIM generally boosts counter-speakers’ satisfaction and perceived effectiveness of their responses, and reduces the difficulty of crafting them, with an exception of gender-focused hate speech. In addition, counterspeech that displayed more empathy, was longer, had a more positive tone, and was associated with higher ratings of effectiveness and perceptions of hatefulness. Prior experience with, and openness to AI writing assistance tools like ChatGPT, correlate negatively with perceived difficulty in writing online counterspeech. Overall, this study contributes insights into linguistic and identity-related factors shaping counterspeech on social media. The findings inform the development of supportive technologies and moderation strategies for promoting effective responses to online hate.

Reexamining Technological Support for Genealogy Research, Collaboration, and Education

Fei Shan, Kurt Luther

Genealogy, the study of family history and lineage, has seen tremendous growth over the past decade, fueled by technological advances such as home DNA testing and mass digitization of historical records. However, HCI research on genealogy practices is nascent, with the most recent major studies predating this transformation. In this paper, we present a qualitative study of the current state of technological support for genealogy research, collaboration, and education. Through semi-structured interviews with 20 genealogists with diverse expertise, we report on current practices, challenges, and success stories around how genealogists conduct research, collaborate, and learn skills. We contrast the experiences of amateurs and experts, describe the emerging importance of standardization and professionalization of the field, and stress the critical role of computer systems in genealogy education. We bridge studies of sensemaking and information literacy through this empirical study on genealogy research practices, and conclude by discussing how genealogy presents a unique perspective through which to study collective sensemaking and education in online communities.

WePilot: Integrating Younger Family Members and Chatbot to Support Older Adults Learning Smartphone Usage

Haonan Zhang, Peng Zhang, Yan Chen, Meitong Guo, Hansu Gu, Tun Lu, Ning Gu

Older adults (OAs) usually face various challenges when using smartphones due to their limited knowledge and the declines in memory and information processing capabilities. Many studies in HCI and CSCW communities have focused on supporting OAs to independently use smartphones. However, compared to independent exploration, support from younger family members (YFMs) has specific advantages in problem understanding, solution personalization, and security protection. However, OAs and YFMs generally have gaps in time, knowledge, and experience, affecting the efficiency of support and their experience. For this problem, we conduct a formative study to gather insights into OAs and YFMs’ perspectives and expectations in the supporting procedure. Then we introduce chatbot to mediate the gaps between OAs and YFMs and build a system named WePilot to assist them to collaboratively solve smartphone usage problems. Evaluations with 12 pairs of participants (OA and corresponding YFM) suggest WePilot’s strengths in improving problem solving efficiency and OAs and YFMs’ experience. Based on these findings, we propose several insights into the future design of intergenerational technical support systems.

Writing Home From Afar: Connecting Distant Families through Sharing of Outdoor Experiences with Digital Diaries

Wei-Lu Wang, Natalie Andrus, Taha Hassan, Jixiang Fan, Yusheng Cao, Joelle Asante, Morva Saaty, Derek Haqq, D. Scott McCrickard

Maintaining emotional connections and fostering meaningful communication among distant family members has long been challenging. Existing communication technologies, such as instant messaging, video-sharing, and social media enable quick exchanges but often lack mechanisms to initiate appropriate conversation topics and support in-depth emotional interactions. This study explores the use of digital diary-sharing in addressing these limitations. We conduct thematic analyses on diaries from a three-week study (N=22) using DailyBean, a diary app, to examine frequent patterns in users' sharing of outdoor experiences with distant family members. We identify five key mechanisms to support connections between distant family members: topic initiation, memory recall, shared moments, joint activities, and future planning. We also highlight frequent conversation topics that facilitate emotional engagement and reflection for distant family members. We conclude our study with design recommendations for effective diary-based family communication.

Details of Workshop Papers

Emotionally intelligent generative AI for genealogy research and collaboration

Fei Shan, Kurt Luther

Emotional intelligence (EI) is critical for effective group collaboration, enabling communication, conflict resolution, and sustained motivation. We investigate the complex emotional experiences inherent in genealogy research, a long-term, demanding collaborative sensemaking task. Through a mixed-method study of amateur genealogists' research, we reveal several key emotional challenges they face during individual research and collaboration. We conclude by discussing specific ways GenAI can be introduced to provide emotional support to enhance both individual and collaborative work.

Towards understanding the impact of generative AI agent roles in collaborative problem-solving tasks

Anirban Mukhopadhyay, Kurt Luther, Kevin Salubre, Shashank Mehrotra, Hifza Javed, Teruhisa Misu, Kumar Akash

Collaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new opportunities to assist, but we know little about how proactive agents affect the dynamics of real-time, co-located teamwork. We designed digital escape rooms as collaborative problem-solving tasks to study two generative AI agents: a Facilitator, which provided discussion summaries and proposed team structures, and a Peer, which contributed ideas as an imperfect teammate. We conducted a within-subjects user study with 24 participants, comparing team performance and processes across three conditions: No AI, Peer AI, and Facilitator AI. Preliminary results show that the Peer agent occasionally enhanced problem-solving by offering timely hints and memory support, though it also disrupted flow and created over-reliance. In comparison, the Facilitator agent provided light scaffolding but had a limited impact on outcomes.

Details of Panels

Scale, Engage, or Both?: Potential and Perils of Applying Large Language Models in Interview and Conversation-Based Research

Angel Hsing-Chi Hwang, Marianne Aubin Le Quéré, Hope Schroeder, Alejandro Cuevas, Steven P. Dow, Shivani Kapania, Eugenia H. Rho

An increasing number of studies apply tools powered by large language models (LLMs) to interview and conversation-based research, one of the most commonly used research methods in CSCW. This panel invites the CSCW community to critically debate the role of LLMs in reshaping interview-based methods. We aim to explore how these tools might (1) address persistent challenges in conversation-based research, such as limited scalability and participant engagement, (2) introduce novel methodological possibilities, and (3) surface additional practical, technical, and ethical concerns. The panel discussion will be grounded on the panelists' prior experience applying LLMs to their own interview and conversation-based research. We ask whether LLMs offer unique advantages to enhance interview research, beyond automating certain aspects of the research process. Through this discussion, we encourage researchers to reflect on how applying LLM tools may require rethinking research design, conversational protocols, and ethical practices.

Details of Posters

Structuring Collaborative Reflection: Integrating Diary Study and Focus Group Discussion

Jixiang Fan, Jiacheng Zhao, Sunggyeol Oh, Michael Bolmer Jr, Yoonje Lee, Nick Flammer, Yuhao Chen, D. Scott McCrickard

We present a structured reflection framework integrating diary study and focus group discussion to support collaborative meaning-making in HCI education. The framework follows a multi-phase design in which students progress from individual journaling to a two-stage group discussion sequence: first within shared application contexts, then across emergent experiential themes. To support this process, we extended DiaryQuest, a lightweight educational tool incorporating AI-assisted grouping, image-based prompts, and a Jigsaw-inspired workflow to scaffold participation. A preliminary classroom deployment with 11 undergraduate students suggests that the approach lowers the barrier to reflective dialogue, encourages cross-perspective engagement, and helps students surface design-relevant insights grounded in lived experience. These findings point to new opportunities for structuring reflection in sociotechnical learning environments.