Biometric payment, i.e., biometric authentication implemented in digital payment systems, can reduce memory demands and streamline payment for older adults. However, older adults’ perceptions and practices regarding biometric payment remain underexplored. We conducted semi-structured interviews with 22 Chinese older adults, including both users and non-users. Participants were motivated to use biometric payment due to convenience and perceived security. However, they also worried about loss of control due to its password-free nature and expressed concerns about biometric data security. Participants also identified desired features for biometric payment, such as lightweight and context-aware cognitive confirmation mechanisms to enhance user control. We outline recommendations for more accessible and informative digital financial services that better support older adults.

Fairness monitoring is critical for detecting algorithmic bias, as mandated by the EU AI Act. Since such monitoring requires sensitive user data (e.g., ethnicity), the AI Act permits its processing only with strict privacy measures, such as multi-party computation (MPC), in compliance with the GDPR. However, the effectiveness of such secure monitoring protocols ultimately depends on people’s willingness to share their data. Little is known about how different MPC protocol designs shape user acceptance. To address this, we conducted an online survey with 833 participants in Europe, examining user acceptance of various MPC protocol designs for fairness monitoring. Findings suggest that users prioritized risk-related attributes (e.g., privacy protection mechanism) in direct evaluation but benefit-related attributes (e.g., fairness objective) in simulated choices, with acceptance shaped by their fairness and privacy orientations. We derive implications for deploying and communicating privacy-preserving protocols in ways that foster informed consent and align with user expectations.

Online fraud substantially harms individuals and seniors are disproportionately targeted. While family is crucial for seniors, little research has empirically examined how they protect seniors against fraud. To address this gap, we employed an inductive thematic analysis of 124 posts and 16,872 comments on RedNote (Xiaohongshu), exploring the family support ecosystem for senior-targeted online fraud in China. We develop a taxonomy of senior-targeted online fraud from a familial perspective, revealing younger members often spot frauds hard for seniors to detect, such as unusual charges. Younger family members fulfill multiple safeguarding roles, including preventative measures, fraud identification, fraud persuasion, loss recovery, and education. They also encounter numerous challenges, such as seniors' refusal of help and considerable mental and financial stress. Drawing on these, we develop a conceptual framework to characterize family support in senior-targeted fraud, and outline implications for researchers and practitioners to consider the broader stakeholder ecosystem and cultural aspects.

Recent research within the HCI community has illuminated the challenges faced by marginalized groups on algorithm-driven livestreaming platforms. However, there is a notable gap in understanding how elderly livestreamers interact with the platform content moderation and algorithmic (in)visibility. This study investigates the perceptions of the algorithm-moderated (in)visibility and the coping strategies of 16 elderly streamers on Douyin. We find that, contrary to stereotypes of elderly users as digitally uninformed, these streamers actively engage with the platform to facilitate their understanding about platform algorithm. This engagement involves official guidance, peer learning, and personal experimentation. The streamers adopt various strategies to align with the perceived algorithmic preferences. Despite their rich knowledge about the platform's visibility moderation, many elderly streamers face significant challenges, such as physical and psychological strain and low viewer traffic. We conclude with design implications for livestreaming platforms to foster fairness and promote engagement among elderly streamers.

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create g'collective knowledge'. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge - a tool incorporating a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced participants' comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design features, we presented design considerations for developing similar support tools.

Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM generated texts. This increases the risk of misuse and highlights the need for reliable detectors. Yet, existing detectors exhibit poor robustness on out-of-distribution (OOD) data and attacked data, which is critical for real-world scenarios. Also, they struggle to provide interpretable evidence to support their decisions, thus undermining reliability. In light of these challenges, we propose IPAD (Inverse Prompt for AI Detection), a novel framework consisting of a Prompt Inverter that identifies predicted prompts that could have generated the input text, and two Distinguishers that examine the probability that the input texts align with the predicted prompts. Empirical evaluations demonstrate that IPAD outperforms the strongest baselines by 9.05% (Average Recall) on in-distribution data, 12.93% (AUROC) on out-of-distribution (OOD) data, and 5.48% (AUROC) on attacked data. IPAD also performs robust on structured datasets. Furthermore, an interpretability assessment is conducted to illustrate that IPAD enhances the AI detection trustworthiness by allowing users to directly examine the decision-making evidence, which provides interpretable support for its state-of-the-art detection results.

Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and overload. To tackle these challenges, we developed EchoAid, a mobile app designed to improve the livestream shopping experience for DHH users. EchoAid utilizes advanced speech-to-text conversion, Rapid Serial Visual Presentation (RSVP) technology, and Large Language Models (LLMs) to simplify the complex information flow in live sales environments. We conducted exploratory studies with eight DHH individuals to identify design needs and iteratively developed the EchoAid prototype based on feedback from three participants. We then evaluate the performance of this system in a user study workshop involving 38 DHH participants. Our findings demonstrate the successful design and validation process of EchoAid, highlighting its potential to enhance product information extraction, leading to reduced cognitive overload and more engaging and customized shopping experiences for DHH users.

People who are deaf or hard of hearing (DHH) in China are increasingly exploring online platforms to connect with potential partners. This research explores the online dating experiences of DHH communities in China, an area that has not been extensively researched. We interviewed sixteen participants who have varying levels of hearing ability and love-seeking statuses to understand how they manage their identities and communicate with potential partners online. We find that DHH individuals made great efforts to navigate the rich modality features to seek love online. Participants used both algorithm-based dating apps and community-based platforms like forums and WeChat to facilitate initial encounters through text-based functions that minimized the need for auditory interaction, thus fostering a more equitable starting point. Community-based platforms were found to facilitate more in-depth communication and excelled in fostering trust and authenticity, providing a more secure environment for genuine relationships. Design recommendations are proposed to enhance the accessibility and inclusiveness of online dating platforms for DHH individuals in China. This research sheds light on the benefits and challenges of online dating for DHH individuals in China and provides guidance for platform developers and researchers to enhance user experience in this area.

The rise of AI-generated content (AIGC) is transforming online creative communities (OCCs) and posing challenges to their regulation. The interacting behaviors, such as sharing artworks with descriptions, commenting on creations, and creators' subsequent replying are the essential components of user engagement in these communities. Understanding the influence of AIGC on the evolving user engagement could be helpful for community regulation. In this work, we collect 235K posts and their associated 255K comments from DeviantArt, a large creative community allowing uploading AIGC. Through open coding, we identify five categories of practices in describing and commenting on artworks, respectively. A set of deep learning models are applied to classify the posts and comments. We then combine time series regression analysis, causal inference analysis, and logistic regression analysis, to examine the impact of the surge of AIGC on user engagement. Results suggest that AI-generated artworks show a decreasing emphasis on the content of creations but an increasing trend toward commercial and promotion purposes. AI-generated artworks emphasize less on IP issues than human-created ones, while the awareness of IP issues drops for human-created artworks with the growth of AIGC as well. Although comments with high sentiment valence, for peer bonding or for requesting usage positively predict the reply behavior for human-created artworks, community members are less likely to maintain these interactions as AIGC rises. Finally, we discuss insights and design implications for OCCs.

Speaking out for women’s daily needs on social media has become a crucial form of everyday feminism in China. Gender debate naturally intertwines with such feminist advocacy, where users in opposite stances discuss gender-related issues through intense discourse. The complexities of gender debate necessitate a systematic understanding of discursive strategies for achieving effective gender communication that balances civility and constructiveness. To address this problem, we adopted a mixed-methods study to navigate discursive strategies in gender debate, focusing on 38,636 posts and 187,539 comments from two representative cases in China. Through open coding, we identified a comprehensive taxonomy of linguistic strategies in gender debate, capturing five overarching themes including derogation, gender distinction, intensification, mitigation, and cognizance guidance. Further, we applied regression analysis to unveil these strategies’ correlations with user participation and response, illustrating the tension between debating tactics and public engagement. We discuss design implications to facilitate feminist advocacy on social media.

Elicitation diary studies, a type of qualitative, longitudinal research method, involve participants to self-report aspects of events of interest at their occurrences as memory cues for providing details and insights during post-study interviews. However, due to time constraints and lack of motivation, participants’ diary entries may be vague or incomplete, impairing their later recall. To address this challenge, we designed an automatic contextual information recording agent, DiaryHelper, based on the theory of episodic memory. DiaryHelper can predict five dimensions of contextual information and confirm with participants. We evaluated the use of DiaryHelper in both the recording period and the elicitation interview through a within-subject study (N=12) over a period of two weeks. Our results demonstrated that DiaryHelper can assist participants in capturing abundant and accurate contextual information without significant burden, leading to a more detailed recall of recorded events and providing greater insights.

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.

Social media images, curated or casual, have become a crucial component of communicating situational information and emotions during health crises. Despite its prevalence and significance in informational dissemination and emotional connection, there lacks a comprehensive understanding of visual crisis communication in the aftermath of a pandemic which is characterized by uncertain local situations and emotional fatigue. To fill this gap, this work collected 345,423 crisis-related posts and 65,376 original images during the Xi'an COVID-19 local outbreak in China, and adopted a mixed-methods approach to understanding themes, goals, and strategies of crisis imagery. Image clustering captured the diversity of visual themes during the outbreak, such as text images embedding authoritative guidelines and "visual diaries" recording and sharing the quarantine life. Through text classification of the post that visuals were situated in, we found that different visual themes highly correlated with the informational and emotional goals of the post text, such as adopting text images to convey the latest policies and sharing food images to express anxiety. We further unpacked nuanced strategies of crisis image use through inductive coding, such as signifying authority and triggering empathy. We discuss the opportunities and challenges of crisis imagery and provide design implications to facilitate effective visual crisis communication.

Understanding livestream platforms’ accessibility challenges for minority groups, such as people with disabilities, is critical to increasing the diversity and inclusion of those platforms. While prior work investigated the experiences of streamers with vision or motor loss, little is known about the experiences of deaf or hard of hearing (DHH) streamers who must work with livestreaming platforms that heavily depend on audio. We conducted semi-structured interviews with DHH streamers to learn why they livestream, how they navigate livestream platforms and related challenges. Our findings revealed their desire to break the stereotypes towards the DHH groups via livestream and the intense interplay between interaction methods, such as sign language, texts, lip language, background music, and viewer characteristics. Major accessibility challenges include the lack of real-time captioning, the small sign language reading window, and misinterpretation of sign language. We present design considerations for improving the accessibility of the livestream platforms.

Livestreamer-moderated matchmaking has gained wide popularity among the elderly population in China. Compared to algorithm-mediated online dating, it is characterized by (1) the mediation of matchmakers in a synchronous virtual environment and (2) the natural development of livestreaming-based matchmaking communities. Nonetheless, how these new features influence single seniors’ match-seeking remains unknown. To fill this research gap, we conduct a qualitative study consisting of observations and semi-structured interviews with 6 livestreaming matchmakers and 12 senior match-seekers (age: 50-70). We uncover matchmakers’ mediation roles during and beyond livestreaming to facilitate seniors’ match-seeking, and their additional duties to enhance seniors’ safety in this process. Livestreaming-based matchmaking communities afford multiple important values for single seniors to acquire companionship and help in seeking late-life love. We unpack the perceived benefits and challenges of livestreamer-moderated matchmaking, and discuss how to support single seniors’ match-seeking in an accessible, safe, and convenient manner.

As a popular form of online media, videos have been widely used to communicate scientific knowledge on video-sharing platforms. These science knowledge videos take advantage of rich and multi-modality information which has the potential to provoke public engagement with science knowledge and promote self-learning. However, how communicators strategically make science knowledge videos to engage viewers, and how specific communication strategies correlate with viewer engagement remain under-explored. In this paper, we first established a taxonomy of communication strategies currently used in science knowledge videos on Bilibili and then examined the correlations between communication strategies and viewers’ behavioral, emotional, and cognitive engagements measured by post-video comments. Our findings revealed the landscape of rich science communication strategies in science knowledge videos and further uncovered the correlations between these strategies and viewer engagements. We situated our results within prior research on science communication and HCI, and provided design implications for video-sharing platforms to support effective science communication.

Mobile payment systems have become an infrastructural component in citizens' socio-economic life in China. The rapid shift to a cashless society demands vendors of all ages to quickly adapt themselves to the ubiquitous mobile payment era. However, how this trend may impact senior vendors, a group that typically uses less technology, remains unknown despite its significance in inclusive mobile payment design. This work aims to address this gap by investigating the challenges and strategies of senior vendors in mobile payment adoption. Particularly, we focus on a traditional low-resource setting with a large volume of senior vendors: street vending. We conduct a qualitative study incorporating field observations on 33 senior street vendors and semi-structured interviews with 15 of them (aged 53-78), and take Moneywork as an analytical lens to unpack their challenges in physical and social interactions. We find that senior street vendors are a group passively adopting mobile payments due to business requirements instead of recognizing their advantages. Vendors with relatively low digital literacy have to take an alternative method - using family members' QR codes - to run the business as family-dependent money receivers. With limited considerations for senior vendors' situational vulnerabilities, unexpected difficulties of payment confirmation emerge during transactions, such as reduced confirmation efficacy under noisy surroundings and degraded hearing. Transaction security issues also appear when mobile payment-based frauds target both confirmation interfaces (e.g., fake sounds of successful payment) and trust systems (e.g., showing half-done proof to flee without paying) in street vending. Finally, we raise a less visible yet critical concern of family-dependent vendors on the lost money freedom when their income flows into their families' wallets. We propose design implications for mobile payment systems to support more secure, efficient and accessible money collection services to underrepresented groups.

Posting help-seeking requests on social media has been broadly adopted by victims during natural disasters to look for urgent rescue and supplies. The help-seeking requests need to get sufficient public attention and be promptly routed to the intended target(s) for timely responses. However, the huge volume and diverse types of crisis-related posts on social media might limit help-seeking requests to receive adequate engagement and lead to their overwhelm. To understand this problem, this work proposes a mixed-methods approach to figure out the overwhelm situation of help-seeking requests, and individuals' and online communities' strategies to cope. We focused on the 2021 Henan Floods in China and collected 141,674 help-seeking posts with the keyword "Henan Rainstorm Mutual Aid" on a popular Chinese social media platform Weibo. The findings indicate that help-seeking posts confront critical challenges of both external overwhelm (i.e., an enormous number of non-help-seeking posts with the help-seeking-related keyword distracting public attention) and internal overwhelm (i.e., attention inequality with 5% help-seeking posts receiving more than 95% likes, comments, and shares). We discover linguistic and non-linguistic help-seeking strategies that could help to prevent the overwhelm, such as including contact information, disclosing situational vulnerabilities, using subjective narratives, and structuring help-seeking posts to a normalized syntax. We also illustrate how community members spontaneously work to prevent the overwhelm with their collective wisdom (e.g., norm development through discussion) and collaborative work (e.g., cross-community support). We reflect on how the findings enrich the literature in crisis informatics and raise design implications that facilitate effective help-seeking on social media during natural disasters.

The wide spread of misinformation contributes to information consumers' excessed distrust of online information. To cope, information consumers are often actively involved in checking the credibility of information through self-researching or seeking help and opinions from experts and peers. While previous studies investigated the factors that affect people's perceptions of information credibility and how laypeople's judgements compare to experts, little is known about how the information credibility assessment work is performed and cooperated by individuals and communities in real-life, natural online environments. Through a qualitative study of an online community, r/DebunkThis, which is dedicated to information debunking, we found that online information debunking rarely followed a linear and straightforward path. Rather, community members, including the debunkers and the original posters, constantly negotiated, and interacted with each other to determine what to debunk and how to debunk. Individuals adopted various strategies to debunk information, such as questioning the credibility of the information source and citing authoritative external information. Community members supplemented with details and explanations, corrected others, requested clarifications, summarized high-level knowledge and skills, and interacted socially based on individuals' debunking explanations. Our study results broaden the understanding of debunking not only as an outcome but also as a learning and social process for community members to learn high-level debunking skills and form and enforce community rules. We provide implications for designing community and crowd-based information debunking systems which should recognize the complex, cooperative, and socially situated work of community and crowd debunkers. The design of such systems should therefore support not only labeling information as correct or not or simply sharing alternative information sources, but also community interactions and learning processes, as well as recognizing the labor of community debunkers.

Gaze-on-screen tracking, an appearance-based eye-tracking task, has drawn significant interest in recent years. While learning-based high-precision eye-tracking methods have been designed in the past, the complex pre-training and high computation in neural network-based deep models restrict their applicability in mobile devices. Moreover, as the display frame rate of mobile devices has steadily increased to 120 fps, high-frame-rate eye tracking becomes increasingly challenging. In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. Specifically, GazeHFR classifies the eye movement into two distinct phases, i.e., saccade and smooth pursuit, and leverages inter-frame motion information combined with lightweight learning models tailored to each movement phase to deliver high-efficient eye tracking without affecting accuracy. Compared to prior art, Gaze-HFR achieves approximately 7x speedup and 15% accuracy improvement on mobile devices.

OBJECTIVE: Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies. MATERIALS AND METHODS: We analyzed a total of 771 268 U.S.-based tweets between January to October 2020. We developed machine learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing. RESULTS: We identified 267 152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about a 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies' websites to support the arguments. CONCLUSIONS: Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

Online videos are playing an increasingly important role in timely information dissemination especially during public crises. Video commentary, synchronous or asynchronous, is indispensable in viewers' engagement and participation, and may in turn contribute to video with additional information and emotions. Yet, the roles of video commentary in crisis communications are largely unexplored, which we believe that an investigation not only provides timely feedback but also offers concrete guidelines for better information dissemination. In this work, we study two distinct commentary features of online videos: traditional asynchronous comments and emerging synchronous danmaku. We investigate how users utilize these two features to express their emotions and share information during a public health crisis. Through qualitative analysis and applying machine learning techniques on a large-scale danmaku and comment dataset of Chinese COVID-19-related videos, we uncover the distinctive roles of danmaku and comments in crisis communication, and propose comprehensive taxonomies for information themes and emotion categories of commentary. We also discover the unique patterns of crisis communications presented by danmaku, such as collective emotional resonance and style-based highlighting for emphasizing critical information. Our study captures the unique values and salient features of the emerging commentary interfaces, in particular danmaku, in the context of crisis videos, and further provides several design implications to enable more effective communications through online videos to engage and empower users during crises.