GoldenTime: Exploring System-Driven Timeboxing and Micro-Financial Incentives for Self-Regulated Phone Use

User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. 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.

Dashcam Witness: Video Sharing Motives and Privacy Concerns across Different Nations

Vehicle dashboard cameras or dashcams, among other smart vehicle technologies, are increasingly attracting interest across the globe. Furthermore, dashcam videos as objective witnesses are often shared to resolve various traffic incidents. In this work, we aim to understand cross-national differences in motives and privacy concerns of dashcam video-sharing, which are closely related to the factors that vary across countries, such as cultural values, traffic regulation, driving environments, and privacy perception.

Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention

We built BeActive, a mobile intervention system for preventing prolonged sedentary behaviors. We collected users' responses taking into account relevant contextual factors such as: what they were doing, where they were, and whom they were with, for three weeks. Using a multi-stage model, we systematically analyzed the responses to deepen our understanding of the receptivity of the JIT intervention.

CampusWatch: Exploring Communitysourced Patrolling with Pervasive Mobile Technology

What do you think of community safety? In this study, we designed a community-sourced patrolling campaign in which community members would schedule their patrol times and routes, perform bike-based patrolling with video capturing and share the information 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.

The results show key findings about users’ task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution, and user motivation and engagement.

Complex and Ambiguous: Understanding Sticker Misinterpretations in Instant Messaging

On computer-mediated communication, stickers or GIFs, though similar in appearance to emoji, have distinct characteristics because they often contain animation, various gestures, and multiple characters and objects. Their complexity and placement constraint may result in miscommunication. In this study, we aim to understand how people perceive emotion in stickers, as well as how miscommunication related to sticker occurs in actual chat contexts via online survey and interview.

Localness of Location-based Knowledge Sharing: A Study of Naver KiN 'Here'

In location-based social Q&A services, people ask a question with a high expectation that local residents who have local knowledge will answer the question. Besides, questions are classified by regions rather than topics, unlike existing topic-based Q&A services. We analyzed a 12-month period Q&A dataset from Naver KiN "Here," a popular location-based social Q&A mobile app in South Korea, in addition to a supplementary survey dataset obtained from 285 mobile users.

Facilitating Pervasive Community Policing on the Road with Mobile Roadwatch

We often see various traffic violations on the road while driving. In this study, we consider community policing on the road with pervasive recording technologies. We developed a mobile app so that drivers can easily capture and report various threats to traffic safety to the police via mobile apps. Our two-week user study showed that the mobile app effectively supported community policing activities on the road.

WatchOut: Facilitating Safe Driving Behaviors with Social Support

What if your parent knows your bad driving habits? Bad driving behaviors are a major cause of traffic accidents, many of them resulting in fatalities. Our study intends to encourage safe driving habits by increasing drivers' self-awareness about their driving habits, as well as receiving supportive feedback on their driving behavior from a loved one as an intervention method. We built an Android prototype app to deliver feedback for bad driving maneuvers to both drivers themselves and to their corresponding supporters and conducted a field study evaluation.

Motives and Concerns of Dashcam Video Sharing

Dashcams support the continuous recording of external views that provide evidence in case of unexpected traffic-related accidents and incidents. Recently, the 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 significantly extend urban surveillance. Our work aims to identify the major motives and concerns behind the sharing of dashcam videos for urban surveillance.

Social or Financial Goals? Comparative Analysis of User Behaviors in Couchsurfing and Airbnb

Uber and Airbnb, two well-known sharing economy services, are facing conflicts with traditional taxi and hotel companies because these services have monetary benefits but are free from legacy regulations. However, non-monetary-based sharing services, represented by Couchsurfing, Inc., are free from such conflict and still successful. We investigated the distinctive user participation motivation of non-monetary-based sharing services versus monetary-based ones.

SlideQA: Supporting Effective Q&A in an Offline Academic Presentation

We aim to design a tool that can facilitate Q&A activities in offline presentations. We first identify several problems associated with current offline Q&A practices. We then address these problems with SlideQA, an online slide-based Q&A tool.

Understanding Localness of Knowledge Sharing

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. 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.

KOREAN PUBLICATIONS

On Campus Dashcam Video Sharing Support System Design for Security Enhancement

Analysis of Graduate Students’ Research Related Q&A Behavior in Campus with Activity Theory, and its System Design Implications

Word Sense Disambiguation using Dynamic Sized Window and Frequency Weighting