Searcharxiv⌕ Search

arXiv · 2610.24207

Navigating Deception on Gay Dating Apps: A Qualitative Study of Users' Experiences, Verification Strategies, and Community Knowledge in China

Abstract

Gay dating applications have become essential platforms for sexual minority men to seek relationships, intimacy, and community, yet they also expose users to a range of deceptive interactions that remain underexplored in HCI and CSCW research. This study examines how gay male users in China experience, identify, and respond to deception on dating applications. Through semi-structured interviews with 22 participants who used platforms such as Blued, Aloha, Fanka, and Soul, we identify a typology of deceptive practices that users encounter, including relational misrepresentation for sexual access, sustained emotional deception, financial fraud, marketing behavior disguised as ordinary users, and false identity construction. We further analyze the verification and coping strategies that users develop through long-term platform use, such as judging relational intent through interactional pace, identifying financial risk signals, recognizing commercial promotion accounts, and progressively verifying identity authenticity. Finally, we show that risk recognition is not merely an individual capacity but a collaborative practice that gradually emerges through the circulation of experience within the community, the summarization of recurrent deceptive tactics, and the learning of shared rules. Our findings contribute to CSCW scholarship by demonstrating how users of a marginalized community collectively construct practical knowledge about deception, and by revealing that trust assessment on dating platforms operates as a layered, probabilistic, and socially distributed process rather than a binary judgment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yibo Meng, Lyumanshan Ye, Bingyi Liu, Huidi Lu, Xiaolan Ding. 2026-10-16. Navigating Deception on Gay Dating Apps: A Qualitative Study of Users' Experiences, Verification Strategies, and Community Knowledge in China. https://arxiv.org/abs/2610.24207

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

JumpStarter: Human-AI Planning with Task-Structured Context Curation

Human-AI collaboration on complex planning goals is bottlenecked by how LLM interfaces handle context: users must manually curate and re-surface relevant information across long and unstructured chat histories. Despite advances in long-context prompting and memory-augmented retrieval, this burden remains unresolved: users still have to identify and supply the right context at each decision point, regardless of how much the model can store or surface. We propose JumpStarter, a system that enables LLMs to collaborate with humans on complex goals by dynamically decomposing tasks to help users manage context. We specifically introduce task-structured context curation, a framework that breaks down a user's goal into a hierarchy of actionable subtasks and scopes context to localized decision points, enabling finer-grained personalization and reuse. The framework is realized through three core mechanisms: context elicitation, selection, and reuse. In a within-subjects user study, plans produced with JumpStarter were rated substantially higher in quality than those produced with ChatGPT. A complementary automatic simulation study shows that JumpStarter consistently outperforms ChatGPT baselines, planning and memory agents, and workflow ablations. These findings show that effective human-AI planning depends not on the volume of context provided, but on attaching the right context to the right subtask at the right time.

cs.HC↗

What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI

Conversational AI tools have been rapidly adopted by students and are becoming part of their learning routines. This widespread use comes amid ongoing concerns about the reliability of AI-generated responses and the appropriate role of AI in education. Drawing on the Technology Acceptance Model, we examine how students' perceptions of conversational AI, including its usefulness, ease of use, trustworthiness, enjoyment, and social influence, relate to their intentions to use these tools for learning. We investigate both the direct and indirect relationships among these perceptions to understand how they are associated with students' behavioral intentions. Using survey data from 229 students and partial least squares structural equation modeling, we found that perceived usefulness was the strongest direct predictor of intention, followed by perceived enjoyment. Perceived ease of use, trust, and subjective norms did not have significant direct associations with intention. However, trust and subjective norms had significant total indirect associations, while perceived enjoyment had both significant direct and indirect associations. The model explained 70.2% of the variance in behavioral intention. These findings suggest that students' intentions to use conversational AI are associated less directly with effort-related considerations and more with perceived usefulness and affective experience. They also highlight the importance of understanding what students perceive as valuable in conversational AI interactions, particularly when designing tools intended to encourage engagement while supporting learning.

cs.HC↗

GazeAgent: VLM-Guided Implicit Gaze Calibration from Egocentric Multimodal Behavior for AI Glasses

AI glasses typically require explicit, user-specific eye-tracker calibration, and accuracy can drift when the device shifts during use. We present GazeAgent, a VLM-guided framework that continuously and adaptively improves gaze-calibration accuracy from naturally occurring behavior without asking users to interrupt their activities. GazeAgent identifies precise visual attention (PVA) events from multimodal streams of inertial measurement unit (IMU), gaze, and wearer-hand signals, validates their task relevance, and localizes functional targets as pseudo-calibration targets. In a 10-participant scripted study, the system detected all 820 predefined PVA opportunities and, under device disturbance, reduced median gaze error by 44.1% compared with explicit calibration. In a five-participant case study, autonomous calibration produced the lowest workload and interruption ratings, and all participants preferred it for routine use. By deriving task-critical targets from natural multimodal behavior, GazeAgent demonstrates the potential for continuous, adaptive, and low-burden gaze interaction on egocentric AI glasses.

cs.HC↗