arXiv subjects

Jingruo Chen

Publications and source records attributed to Jingruo Chen.

9 recordsLinked to original sources

arXiv2026

CoVer: Conflict-Aware Claim Verification

Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative news sources. To capture this challenge and support conflict verification tasks, we present ContraNote, a large-scale real-world dataset curated from X's Community Notes system. It includes 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level prioritization. Additionally, we propose CoVer, a factual adjudication framework with three-stage pipelines: evidence schema normalization, factual consensus and support verification. This prioritizes evidence over noise to prevent it from compromising the final verdict. Technical evaluations show that CoVer achieves strong performance compared with state-of-the-art baselines across ContraNote (86.0% Acc., 68.0% mac. F1, 64.5 bal. Acc. on Conflict; and 88.5% Acc., 88.5 mac. F1 and 89.2 bal. Acc. on Prioritization), CONFACT-HumC (88.4% Acc.) and CONFACT-ModC (89.4% Acc.).

cs.AI↗

arXiv2026

Balancing Safety and Autonomy: Accessibility-Oriented Interventions in Generative AI for Cognitive Impairment

Generative AI systems are increasingly used by older adults with cognitive impairment for everyday tasks such as information seeking, health management, and communication. While these systems provide flexible, language-based support, their open-ended outputs introduce risks of over-reliance, misinterpretation, and inappropriate decision-making. Prior work has focused on usability and adoption, with limited attention to how system design shapes users' participation in decision-making and the distribution of agency in care contexts. We present a qualitative study of 45 individuals with cognitive impairment and their caregivers. We identify five accessibility-oriented mechanisms: AI Capability Constraint, Human Oversight Embedding, Cognitive Engagement Maintenance, Human-AI Relationship Regulation, and Risk Transparency and Control, through which systems structure interaction. These mechanisms both support and constrain users by redistributing decision-making across users and caregivers. We show that their effects vary by impairment level: while protective mechanisms support users with severe impairment, they can restrict autonomy for those with mild impairment. As impairment progresses, tensions become less visible as user participation diminishes. Our findings highlight the need for dynamic designs that balance safety and autonomy in AI-supported care.

cs.HC↗

arXiv2026

CANote: Empowering Fact-checking Note Writing Through Scaffolded and Provenance-based Human-AI Collaboration

Crowdsourced fact-checking mechanisms, such as X's Community Notes, play a critical role in mitigating the spread of misinformation. However, drafting high-quality, evidence-based debunking notes imposes a substantial burden on contributors. We present CANote, an AI-assisted debunking note writing system featuring evidence correlation and structured co-drafting. CANote scaffolds the workflow by extracting subclaims from social media posts, providing provenance through explicit links between subclaims and retrieved evidence, and generating neutral, structural drafts to support human reasoning. We evaluated CANote against manual writing (N=52 fact-checkers, N=52 lay users) on simulated X platform, where we found CANote significantly improves note quality. Notably, CANote enables lay users to write notes that have comparable quality to those written by experts. While the task completion time and perceived cognitive load remain comparable to manual drafting, CANote significantly increases user satisfaction. However, this assistance introduces a trade-off, resulting in a reduced sense of user ownership and control over the debunking note.

cs.HC↗

arXiv2026

"Not Just Me and My To-Do List": Understanding Challenges of Task Management for Adults with ADHD and the Need for AI-Augmented Social Scaffolds

Adults with ADHD often face challenges with task management, not due to a lack of willpower, but because of emotional and relational misalignments between cognitive needs and normative infrastructures. Existing productivity tools, designed for neurotypical users, often assume consistent self-regulation and linear time, overlooking these differences. We conducted 22 semi-structured interviews with ADHD-identifying adults, exploring their challenges in task management and their coping mechanisms through socially and emotionally scaffolded strategies. Building on these insights, we conducted a follow-up speed dating study with 20 additional ADHD-identifying adults, focusing on 13 speculative design concepts that leverage AI for task support. Our findings reveal that task management among adults with ADHD is relationally and affectively co-constructed, rather than an isolated individual act. Overall, we provide (1) empirical insights into distributed and emotionally scaffolded task management practices, (2) design implications for socially-aware AI systems that support co-regulation and nonlinear attention rhythms, and (3)an analysis of user preferences for different AI design concepts, clarifying which features were most valued and why.

cs.HC↗

arXiv2025

Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media

AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that increasing label detail enhances user perceptions of label transparency but does not affect user engagement. However, content stakes significantly impact user engagement and perceptions, with users demonstrating higher engagement and trust in low-stakes images. These results suggest that social media platforms can adopt detailed labels to improve transparency without compromising user engagement, offering insights for effective labeling strategies for AI-generated content.

cs.HC↗

arXiv2025

Fact-Checking Comments Precede Community Notes to Misleading Posts on X

Community-based fact-checking systems, such as X's Community Notes program, offer a potentially scalable approach against online misinformation diffusion. However, their efficacy is substantially undermined by the delay of fact-check delivery. To address this challenge, we investigated the promise of organic user comments as rapid corrective signals by analyzing a large-scale dataset of over 2.2 million comments directed to 1,841 community fact-checked misleading posts. We developed a high-performance language model pipeline to identify ``fact-checking (FC) comments'', i.e., comments that correct source posts with reasoning or evidence (91% accuracy). Using this pipeline, we find that 99.4% of misleading posts receive their first FC comments before the official community note is created. Notably, the median time to the initial FC comment is only 0.1 hours, while the creation of community notes has a median latency of 9.7 hours since the publication of misleading posts. Additionally, compared to random posts, those misleading posts with displayed notes have significantly more FC comments. This suggests that FC comments are a unique pattern for misleading posts. We further identified characteristics of FC comments' intensity and speed: (i) FC comments' volume is significantly associated with content richness and misinformation type, favoring multimedia content and missing-context scenarios; (ii) the speed of FC comments remains robust across diverse topics and emotional intensities. These results suggest that platforms could use FC comments as early-warning signals, and potential resource for complementing community notes or automated synthesis.

cs.HC↗

arXiv2025

Towards Aligning Personalized Conversational Recommendation Agents with Users' Privacy Preferences

The proliferation of AI agents, with their complex and context-dependent actions, renders conventional privacy paradigms obsolete. This position paper argues that the current model of privacy management, rooted in a user's unilateral control over a passive tool, is inherently mismatched with the dynamic and interactive nature of AI agents. We contend that ensuring effective privacy protection necessitates that the agents proactively align with users' privacy preferences instead of passively waiting for the user to control. To ground this shift, and using personalized conversational recommendation agents as a case, we propose a conceptual framework built on Contextual Integrity (CI) theory and Privacy Calculus theory. This synthesis first reframes automatically controlling users' privacy as an alignment problem, where AI agents initially did not know users' preferences, and would learn their privacy preferences through implicit or explicit feedback. Upon receiving the preference feedback, the agents used alignment and Pareto optimization for aligning preferences and balancing privacy and utility. We introduced formulations and instantiations, potential applications, as well as five challenges.

cs.HC↗

arXiv2025

Characterizing Unintended Consequences in Human-GUI Agent Collaboration for Web Browsing

The proliferation of Large Language Model (LLM)-based Graphical User Interface (GUI) agents in web browsing scenarios present complex unintended consequences (UCs). This paper characterizes three UCs from three perspectives: phenomena, influence and mitigation, drawing on social media analysis (N=221 posts) and semi-structured interviews (N=14). Key phenomenon for UCs include agents' deficiencies in comprehending instructions and planning tasks, challenges in executing accurate GUI interactions and adapting to dynamic interfaces, the generation of unreliable or misaligned outputs, and shortcomings in error handling and feedback processing. These phenomena manifest as influences from unanticipated actions and user frustration, to privacy violations and security vulnerabilities, and further to eroded trust and wider ethical concerns. Our analysis also identifies user-initiated mitigation, such as technical adjustments and manual oversight, and provides implications for designing future LLM-based GUI agents that are robust, user-centric, and transparent, fostering a crucial balance between automation and human oversight.

cs.HC↗

arXiv2024

SituFont: A Just-in-Time Adaptive Intervention System for Enhancing Mobile Readability in Situational Visual Impairments

Situational visual impairments (SVIs) hinder mobile readability, causing discomfort and limiting information access. Building on prior work in adaptive typography and accessibility, this paper presents SituFont, a context-aware and human-in-the-loop adaptive typography adjustment approach that enhances smartphone mobile readability by dynamically adjusting font parameters based on real-time contextual changes. Using smartphone sensors and a human-in-the-loop approach, SituFont personalizes text presentation to accommodate personal factors (e.g., fatigue, distraction) and environmental conditions (e.g., lighting, motion, location). To inform its design, we conducted formative interviews (N=15) to identify key SVI factors and controlled experiments (N=18) to quantify their impact on optimal text parameters. A comparative user study (N=12) across eight simulated SVI scenarios demonstrated SituFont's effectiveness in improving smartphone mobile readability in terms of improved efficiency and reduced workload compared with a non-trivial manual adjustment baseline.

cs.HC↗