arXiv subjects

Simo Hosio

Publications and source records attributed to Simo Hosio.

At least 19 recordsLinked to original sources

arXiv2026

"Nobody Did This": Contribution, Originality, and Accountability in Agent-Mediated Collaboration

Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contributions can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.

cs.CY↗

arXiv2026

RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit

Community-conditioned language model adaptation needs choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts. We present RedditPersona, a modular framework that standardizes these choices: it collects Reddit posts and comments, profiles active users, partitions them under five grouping strategies (subreddit-based, graph-structural, semantic, hybrid, and interaction-based), trains a parameter-efficient adapter per strategy via QLoRA, and evaluates them under a shared metric suite spanning fluency, fidelity, distributional alignment, and community identifiability. Applied to 112 subreddits in the urban well-being domain (301,429 user profiles, 16M+ comments), we find that adapters' behavioral identifiability tracks each strategy's agreement with the subreddit baseline, and that a consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies. The code and configuration files are available at: https://github.com/Ahghaffari/redditpersona.

cs.AI↗

arXiv2026

Robot-Wearable Conversation Hand-off for Navigation

Navigating large and complex indoor environments, such as universities, airports, and hospitals, can be cognitively demanding and requires attention and effort. While mobile applications provide convenient navigation support, they occupy the user's hands and visual attention, limiting natural interaction. In this paper, we explore conversation hand-off as a method for multi-device indoor navigation, where a Conversational Agent (CA) transitions seamlessly from a stationary social robot to a wearable device. We evaluated robot-only, wearable-only, and robot-to-wearable hand-off in a university campus setting using a within-subjects design with N=24 participants. We find that conversation hand-off is experienced as engaging, even though no performance benefits were observed, and most preferred using the wearable-only system. Our findings suggest that the design of such re-embodied assistants should maintain a shared voice and state across embodiments. We demonstrate how conversational hand-offs can bridge cognitive and physical transitions, enriching human interaction with embodied AI.

cs.HC↗

arXiv2026

Conversational Inoculation to Enhance Resistance to Misinformation

Proliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation through dynamic conversations with a chatbot. We built a Web-based system to implement the method, and conducted a within-subject user experiment to compare it with two traditional inoculation methods. Our results validate Conversational Inoculation as a viable novel method, and show how it was able to enhance participants' resistance to misinformation. A qualitative analysis of the conversations between participants and the chatbot reveal independence and trust as factors that boosted the efficiency of Conversational Inoculation, and friction of interaction as a factor hindering it. We discuss the opportunities and challenges of using Conversational Inoculation to combat misinformation. Our work contributes a timely investigation and a promising research direction in scalable ways to combat misinformation.

cs.HC↗

arXiv2026

Watching AI Think: User Perceptions of Visible Thinking in Chatbots

People increasingly turn to conversational agents such as ChatGPT to seek guidance for their personal problems. As these systems grow in capability, many now display elements of "thinking": short reflective statements that reveal a model's intentions or values before responding. While initially introduced to promote transparency, such visible thinking can also anthropomorphise the agent and shape user expectations. Yet little is known about how these displays affect user perceptions in help-seeking contexts. We conducted a 3 x 2 mixed design experiment examining the impact of 'Thinking Content' (None, Emotionally-Supportive, Expertise-Supportive) and 'Conversation Context' (Habit-related vs. Feelings-related problems) on users' perceptions of empathy, warmth, competence, and engagement. Participants interacted with a chatbot that either showed no visible thinking or presented value-oriented reflections prior to its response. Our findings contribute to understanding how thinking transparency influences user experience in supportive dialogues, and offer implications for designing conversational agents that communicate intentions in sensitive, help-seeking scenarios.

cs.HC↗

arXiv2025

Two Modes of Reflection: How Temporal, Spatial, and Social Distances Affect Reflective Writing in Family Caregiving

Writing about personal experiences can improve well-being, but for family caregivers, fixed or user-initiated schedules often miss the right moments. Drawing on Construal Level Theory, we conducted a three-week field study with 47 caregivers using a chatbot that delivered daily reflective writing prompts and captured temporal, spatial, and social contexts. We collected 958 writing entries, resulting in 5,412 coded segments. Our Analysis revealed two reflective modes. Under proximal conditions, participants produced detailed, emotion-rich, and care recipient-focused narratives that supported emotional release. Under distal conditions, they generated calmer, self-focused, and analytic accounts that enabled objective reflection and cognitive reappraisal. Participants described trade-offs: proximity preserved vivid detail but limited objectivity, while distance enabled analysis but risked memory loss. This work contributes empirical evidence of how psychological distances shape reflective writing and proposes design implications for distance-aware Just-in-Time Adaptive Interventions for family caregivers' mental health support.

cs.HC↗

arXiv2025

Quo Vadis, HCOMP? A Review of 12 Years of Research at the Frontier of Human Computation and Crowdsourcing

The field of human computation and crowdsourcing has historically studied how tasks can be outsourced to humans. However, many tasks previously distributed to human crowds can today be completed by generative AI with human-level abilities, and concerns about crowdworkers increasingly using language models to complete tasks are surfacing. These developments undermine core premises of the field. In this paper, we examine the evolution of the Conference on Human Computation and Crowdsourcing (HCOMP) - a representative example of the field as one of its key venues - through the lens of Kuhn's paradigm shifts. We review 12 years of research at HCOMP, mapping the evolution of HCOMP's research topics and identifying significant shifts over time. Reflecting on the findings through the lens of Kuhn's paradigm shifts, we suggest that these shifts do not constitute a paradigm shift. Ultimately, our analysis of gradual topic shifts over time, combined with data on the evident overlap with related venues, contributes a data-driven perspective to the broader discussion about the future of HCOMP and the field as a whole.

cs.CY↗

arXiv2025

Cognitive Performance Measurements and the Impact of Sleep Quality Using Wearable and Mobile Sensors

Human cognitive performance is an underlying factor in most of our daily lives, and numerous factors influence cognitive performance. In this work, we investigate how changes in sleep quality influence cognitive performance, measured from a dataset collected during a 2-month field study. We collected cognitive performance data (alertness) with the Psychomotor Vigilance Task (PVT), mobile keyboard typing metrics from participants' smartphones, and sleep quality metrics through a wearable sleep tracking ring. Our findings highlight that specific sleep metrics like night-time heart rate, sleep latency, sleep timing, sleep restfulness, and overall sleep quantity significantly influence cognitive performance. To strengthen the current research on cognitive measurements, we introduce smartphone typing metrics as a proxy or a complementary method for continuous passive measurement of cognitive performance. Together, our findings contribute to ubiquitous computing via a longitudinal case study with a novel wearable device, the resulting findings on the association between sleep and cognitive function, and the introduction of smartphone keyboard typing as a proxy of cognitive function.

cs.HC↗

arXiv2025

Keeping Score: A Quantitative Analysis of How the CHI Community Appreciates Its Milestones

The ACM CHI Conference has a tradition of citing its intellectual heritage. At the same time, we know CHI is highly diverse and evolving. In this highly dynamic context, it is not clear how the CHI community continues to appreciate its milestones (within and outside of CHI). We present an investigation into how the community's citations to milestones have evolved over 43 years of CHI Proceedings (1981-2024). Forgetting curves plotted for each year suggest that milestones are slowly fading from the CHI community's collective memory. However, the picture is more nuanced when we trace citations to the top-cited milestones over time. We identify three distinct types of milestones cited at CHI, a typology of milestone contributions, and define the Milestone Coefficient as a metric to assess the impact of milestone papers on a continuous scale. Further, we provide empirical evidence of a Matthew effect at CHI. We discuss the broader ramifications for the CHI community and the field of HCI.

cs.HC↗

arXiv2024

From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.

cs.HC↗

arXiv2024

LMLPA: Language Model Linguistic Personality Assessment

Large Language Models (LLMs) are increasingly used in everyday life and research. One of the most common use cases is conversational interactions, enabled by the language generation capabilities of LLMs. Just as between two humans, a conversation between an LLM-powered entity and a human depends on the personality of the conversants. However, measuring the personality of a given LLM is currently a challenge. This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs. Our system helps to understand LLMs' language generation capabilities by quantitatively assessing the distinct personality traits reflected in their linguistic outputs. Unlike traditional human-centric psychometrics, the LMLPA adapts a personality assessment questionnaire, specifically the Big Five Inventory, to align with the operational capabilities of LLMs, and also incorporates the findings from previous language-based personality measurement literature. To mitigate sensitivity to the order of options, our questionnaire is designed to be open-ended, resulting in textual answers. Thus, the AI rater is needed to transform ambiguous personality information from text responses into clear numerical indicators of personality traits. Utilising Principal Component Analysis and reliability validations, our findings demonstrate that LLMs possess distinct personality traits that can be effectively quantified by the LMLPA. This research contributes to Human-Computer Interaction and Human-Centered AI, providing a robust framework for future studies to refine AI personality assessments and expand their applications in multiple areas, including education and manufacturing.

cs.CL↗

arXiv2024

When Group Spirit Meets Personal Journeys: Exploring Motivational Dynamics and Design Opportunities in Group Therapy

Psychotherapy, such as cognitive-behavioral therapy (CBT), is effective in treating various mental disorders. Technology-facilitated mental health therapy improves client engagement through methods like digitization or gamification. However, these innovations largely cater to individual therapy, ignoring the potential of group therapy-a treatment for multiple clients concurrently, which enables individual clients to receive various perspectives in the treatment process and also addresses the scarcity of healthcare practitioners to reduce costs. Notwithstanding its cost-effectiveness and unique social dynamics that foster peer learning and community support, group therapy, such as group CBT, faces the issue of attrition. While existing medical work has developed guidelines for therapists, such as establishing leadership and empathy to facilitate group therapy, understanding about the interactions between each stakeholder is still missing. To bridge this gap, this study examined a group CBT program called the Serigaya Methamphetamine Relapse Prevention Program (SMARPP) as a case study to understand stakeholder coordination and communication, along with factors promoting and hindering continuous engagement in group therapy. In-depth interviews with eight facilitators and six former clients from SMARPP revealed the motivators and demotivators for facilitator-facilitator, client-client, and facilitator-client communications. Our investigation uncovers the presence of discernible conflicts between clients' intrapersonal motivation as well as interpersonal motivation in the context of group therapy through the lens of self-determination theory. We discuss insights and research opportunities for the HCI community to mediate such tension and enhance stakeholder communication in future technology-assisted group therapy settings.

cs.HC↗

arXiv2024

What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor

Background: Humor is a fundamental part of human communication, with prior work linking positive humor in the workplace to positive outcomes, such as improved performance and job satisfaction. Aims: This study aims to investigate programming-related humor in a large social media community. Methodology: We collected 139,718 submissions from Reddit subreddit r/ProgrammerHumor. Both textual and image-based (memes) submissions were considered. The image data was processed with OCR to extract text from images for NLP analysis. Multiple regression models were built to investigate what makes submissions humorous. Additionally, a random sample of 800 submissions was labeled by human annotators regarding their relation to theories of humor, suitability for the workplace, the need for programming knowledge to understand the submission, and whether images in image-based submissions added context to the submission. Results: Our results indicate that predicting the humor of software developers is difficult. Our best regression model was able to explain only 10% of the variance. However, statistically significant differences were observed between topics, submission times, and associated humor theories. Our analysis reveals that the highest submission scores are achieved by imagebased submissions that are created during the winter months in the northern hemisphere, between 2-3pm UTC on weekends, which are distinctly related to superiority and incongruity theories of humor, and are about the topic of "Learning". Conclusions: Predicting humor with natural language processing methods is challenging. We discuss the benefits and inherent difficulties in assessing perceived humor of submissions, as well as possible avenues for future work. Additionally, our replication package should help future studies and can act as a joke repository for the software industry and education.

cs.SE↗

arXiv2024

Examining Input Modalities and Visual Feedback Designs in Mobile Expressive Writing

Expressive writing is an established approach for stress management. Recently, information technologies, such as smartphones, have also been explored for expressive writing. Although mobile interfaces have the potential to support various daily writing activities, interface designs for mobile expressive writing and their effects on stress relief still lack empirical understanding. We examined the interface design of mobile expressive writing by investigating the influence of input modalities and visual feedback designs on usability and perceived cathartic effects through field studies. While our studies confirmed the stress-relieving effects of mobile expressive writing, our results offer important insights into interface design. We found keyboard-based text entry more suited and preferred over voice messages for its privacy and reflective nature. Participants expressed different reasons for preferring different post-writing visual feedback depending on the cause and type of stress. This work advances interface design for mobile expressive writing and deepens understanding of its effects.

cs.HC↗

arXiv2023

What if we have Meta GPT? From Content Singularity to Human-Metaverse Interaction in AIGC Era

The global metaverse development is facing a "cooldown moment", while the academia and industry attention moves drastically from the Metaverse to AI Generated Content (AIGC) in 2023. Nonetheless, the current discussion rarely considers the connection between AIGCs and the Metaverse. We can imagine the Metaverse, i.e., immersive cyberspace, is the black void of space, and AIGCs can simultaneously offer content and facilitate diverse user needs. As such, this article argues that AIGCs can be a vital technological enabler for the Metaverse. The article first provides a retrospect of the major pitfall of the metaverse applications in 2022. Second, we discuss from a user-centric perspective how the metaverse development will accelerate with AIGCs. Next, the article conjectures future scenarios concatenating the Metaverse and AIGCs. Accordingly, we advocate for an AI-Generated Metaverse (AIGM) framework for energizing the creation of metaverse content in the AIGC era.

cs.HC↗

arXiv2023

Revealing complexities when adult readers engage in the credibility evaluation of social media posts

The present study investigates the role of source characteristics, evidence quality, crowdsourcing platform, and prior beliefs of the topic in adult readers' credibility evaluations of short healthrelated social media posts. Researchers designed content for the posts concerning five health topics by manipulating source characteristics (source's expertise, gender, and ethnicity), accuracy of the claims, and evidence quality (research evidence, testimony, consensus, and personal experience) in the posts. Then, accurate and inaccurate posts varying in these other manipulated aspects were computer-generated. Crowdworkers (N = 844) recruited from two platforms were asked to evaluate the credibility of ten social media posts, resulting in 8380 evaluations. Before credibility evaluation, participants' prior beliefs on the topics of the posts were assessed. Results showed that prior belief consistency and source expertise most affected the perceived credibility of accurate and inaccurate social media posts after controlling for the topic of the post. In contrast, the quality of evidence supporting the health claim mattered relatively little. In addition, the data collection platform had a notable impact, such that posts containing inaccurate claims were much more likely to be rated higher on one platform compared to the other. Implications for credibility evaluation theory and research are discussed.

cs.SI↗

arXiv2022

Beyond the Blue Sky of Multimodal Interaction: A Centennial Vision of Interplanetary Virtual Spaces in Turn-based Metaverse

Human habitation across multiple planets requires communication and social connection between planets. When the infrastructure of a deep space network becomes mature, immersive cyberspace, known as the Metaverse, can exchange diversified user data and host multitudinous virtual worlds. Nevertheless, such immersive cyberspace unavoidably encounters latency in minutes, and thus operates in a turn-taking manner. This Blue Sky paper illustrates a vision of an interplanetary Metaverse that connects Earthian and Martian users in a turn-based Metaverse. Accordingly, we briefly discuss several grand challenges to catalyze research initiatives for the `Digital Big Bang' on Mars.

cs.HC↗

arXiv2022

From Digital Media to Empathic Reality: A Systematic Review of Empathy Research in Extended Reality Environments

Recent advances in extended reality (XR) technologies have enabled new and increasingly realistic empathy tools and experiences. In XR, all interactions take place in different spatial contexts, all with different features, affordances, and constraints. We present a systematic literature survey of recent work on empathy in XR. As a result, we contribute a research roadmap with three future opportunities in XR-enabled empathy research across both physical and virtual spaces.

cs.HC↗