The growing trend of sharing short videos on social media platforms, where users capture and share moments from their daily lives, has led to an increase in research efforts focused on micro-video recommendations. However, conventional methods oversimplify the modeling of skip behavior, categorizing interactions solely as positive or negative based on whether skipping occurs. This study was motivated by the importance of the first few seconds of micro-videos, leading to a refinement of signals into three distinct categories: highly positive, less positive, and negative. Specifically, we classify skip interactions occurring within a short time as negatives, while those occurring after a delay are categorized as less positive. The proposed dual-level graph and hierarchical ranking loss are designed to effectively learn these fine-grained interactions. Our experiments demonstrated that the proposed method outperformed three conventional methods across eight evaluation measures on two public datasets.

User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. These tools basically assume active user engagement, but prior studies warned a lack of user engagement over time. This paper proposes GoldenTime, a mobile app that promotes self-regulated usage behavior via system-driven proactive timeboxing and micro-financial incentives framed as gain or loss for behavioral reinforcement. We conducted a large-scale user study (n = 210) to explore how our proactive timeboxing and micro-financial incentives influence users’ smartphone usage behaviors. Our findings show that GoldenTime’s timeboxing based micro-financial incentives are effective in self-regulating smartphone usage, and incentive framing has a significant impact on user behavior. We provide practical design guidelines for persuasive technology design related to promoting digital wellbeing.

Stickers, though similar in appearance to emoji, have distinct characteristics because they often contain animation, diverse gestures, and multiple characters and objects. Stickers can convey richer meaning than emoji, but their complexity and placement constraint may result in miscommunication. In this paper, we aim to understand how people perceive emotion in stickers, as well as how miscommunication related to sticker occurs in actual chat contexts. Toward this goal, we conducted an online survey (n = 87) and in-depth interviews (n = 28) in South Korea. We found emotional and contextual aspects of sticker misinterpretation. In particular, emotion misinterpretation mostly happened due to stickers' ambiguous (multiple) facial/bodily expressions and different perception of dynamism in gestures. In real chat settings, there were also contextual misinterpretations where senders and receivers differently interpret stickers' visual representation/reference, or/and corresponding textual messages. Based on these findings, we provide several practical design implications such as context awareness support in sticker interaction.

Community policing to collaboratively maintain community safety and order in conjunction with law enforcement is becoming increasingly popular and efficient with the use of mobile technologies. Beyond sharing information about local problems such as crime via online discussion forums, there has been an increased focus on the impact of mobile crowdsourcing systems on community policing. In this study, we designed a novel crowdsourced patrolling campaign in which community members schedule their own patrol times and routes, then perform bike-based patrolling with video capturing using their smartphones. We conducted a four-week field study (n=20) on a university campus to verify the campaign's feasibility and observe users' behavior. Our results show key findings about users' task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution and user motivation and engagement. Finally, we discuss several practical design implications in building crowdsourcing systems for community policing.

In location-based social Q8A services, people ask a question with a high expectation that local residents who have local knowledge will answer the question. However, little is known about the locality of user activities in location-based social Q8A services. This study aims to deepen our understanding of location-based knowledge sharing by investigating the following: general behavioral characteristics of users, the topical and typological patterns related to geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q8A dataset from Naver KiN “Here,” a location-based social Q8A mobile app, in addition to a supplementary survey dataset obtained from 285 mobile users. Our results reveal several unique characteristics of location-based social Q8A. When compared with conventional social Q8A sites, users ask and answer different topical/typological questions. In addition, those who answer have a strong spatial locality wherein they primarily have local knowledge in a few regions, in areas such as their home and work. We also find unique motivators such as ownership of local knowledge and a sense of local community. The findings reported in the article have significant implications for the design of Q8A systems, especially location-based social Q8A systems.

We consider community policing on the road with pervasive recording technologies such as dashcams and smartphones where citizens are actively volunteering to capture and report various threats to traffic safety to the police via mobile apps. This kind of novel community policing has recently gained significant popularity in Korea and India. In this work, we identify people's general attitude and concerns toward community policing on the road through an online survey. We then address the major concerns by building a mobile app that supports easy event capture/access, context tagging, and privacy preservation. Our two-week user study (n = 23) showed Roadwatch effectively supported community policing activities on the road. Further, we found that the critical factors for reporting are personal involvement and seriousness of risks, and participants were mainly motivated by their contribution to traffic safety. Finally, we discuss several practical design implications to facilitate community policing on the road.

Dashcams support continuous recording of external views that provide evidence in case of unexpected traffic-related accidents and incidents. Recently, sharing of dashcam videos has gained significant traction for accident investigation and entertainment purposes. Furthermore, there is a growing awareness that dashcam video sharing will greatly extend urban surveillance. Our work aims to identify the major motives and concerns behind the sharing of dashcam videos for urban surveillance. We conducted two survey studies (n=108, n=373) in Korea. Our results show that reciprocal altruism/social justice and monetary reward were the major motives and that participants were strongly motivated by altruism and social justice. Our studies have also identified major privacy concerns and found that groups with greater privacy concerns had lower altruism and justice motive, but had higher monetary motive. Our main findings have significant implications on the design of a dashcam video-sharing service.

In location-based social Q&A, the questions related to a local community (e.g. local services and places) are typically answered by local residents (i.e. who have the local knowledge). In this work, we wanted to deepen our understanding of the localness of knowledge sharing through investigating the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN "Here" and a supplementary survey dataset from 285 mobile users. Our results revealed several unique characteristics of location-based social Q&A. When compared with conventional social Q&A sites, Naver KiN "Here" had very different topical/typological patterns. Naver KiN "Here" exhibited a strong spatial locality where the answerers mostly had 1-3 spatial clusters of contributions, the topical distributions varied widely across different districts, and a typical cluster spanned a few neighboring districts. In addition, we uncovered unique motivators, e.g. ownership of local knowledge and sense of local community. The findings reported in the paper have significant implications for the design of Q&A systems, especially location-based social Q&A systems.