HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web SystemConsiderable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives.2024YJYoungseung Jeon et al.Session 2e: Echo Chambers and Fake News in FocusCSCW
PRECYSE: Predicting Cybersickness using Transformer for Multimodal Time-Series Sensor DataJeong 等人提出PRECYSE框架，利用Transformer融合头部运动、眼动和生理信号等多模态时序数据，实现VR场景下网络晕动症的提前预测。2024DJDayoung Jeong et al.Motion Sickness & Passenger ExperienceEye Tracking & Gaze InteractionBiosensors & Physiological MonitoringUbiComp
ChamberBreaker: Mitigating the Echo Chamber Effect and Supporting Information Hygiene through a Gamified Inoculation SystemBecause of the increasingly negative impacts of the echo chamber effect, such as the dissemination of fake news and political polarization occurring in social networking services (SNSs), considerable efforts are being made to mitigate this effect. Prior HCI studies have presented the development of user interfaces to display information that reflects various standpoints, with the aim of nudging people to consume information in a more objective fashion. However, these efforts still lack the ability to highlight the characteristics, generation processes, and negative effects of echo chambers, so they may not be effective in helping people become sufficiently aware of the echo chamber effect and those who are already in an echo chamber. In this paper, we present ChamberBreaker (CB), which has been designed to help increase a player's awareness of and preemptively respond to an echo chamber effect based on psychological concepts: inoculation, heuristics for judging, and gamification. Through a user study with 882 participants (control group: 446, experimental group: 436), we demonstrated the feasibility of our game-based methodology to support the awareness of the echo chamber effect and the importance of maintaining diverse perspectives when consuming information. Our findings highlight the externalization of psychological standpoints in mitigating an echo chamber effect and suggest design implications for system development---the consideration of demographics, playing time, and the connection to fake news recognition---for digital literacy education. You can play CB at http://tiny.cc/chamberbreaker2021YJYoungseung Jeon et al.Civic Engagement, Politics, and PolarizationCSCW
FashionQ: An AI-Driven Creativity Support Tool for Facilitating Ideation in Fashion DesignRecent research on creativity support tools (CST) adopts artificial intelligence (AI) that leverages big data and computational capabilities to facilitate creative work. Our work aims to articulate the role of AI in supporting creativity with a case study of an AI-based CST tool in fashion design based on theoretical groundings. We developed AI models by externalizing three cognitive operations (extending, constraining, and blending) that are associated with divergent and convergent thinking. We present FashionQ, an AI-based CST that has three interactive visualization tools (StyleQ, TrendQ, and MergeQ). Through interviews and a user study with 20 fashion design professionals (10 participants for the interviews and 10 for the user study), we demonstrate the effectiveness of FashionQ on facilitating divergent and convergent thinking and identify opportunities and challenges of incorporating AI in the ideation process. Our findings highlight the role and use of AI in each cognitive operation based on professionals’ expertise and suggest future implications of AI-based CST development.2021YJYoungseung Jeon et al.Ajou UniversityGenerative AI (Text, Image, Music, Video)Graphic Design & Typography ToolsCreative Collaboration & Feedback SystemsCHI
Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress ManagementStress is caused by a variety of events in our daily lives. By anticipating stressful situations, we can prepare and better cope with stressors when they actually occur. However, many past-centric personal informatics (PI) tools focus on capturing events that already happened and analyzing the data. In this work, we examine how anticipation a future-centric self-tracking practice could be used to manage daily stress levels. To address this, we built MindForecaster, a calendar- mediated stress anticipation application that allows users to expect stressful events in advance, generates activities to mitigate stress, and evaluates actual stress levels compared to previously estimated stress levels. In a 30-day deployment with 47 users, the users who explicitly planned and executed coping interventions reported reduced stress more than those who only expected stressful events. We suggest design implications for stress management by incorporating the properties of anticipation into current PI models.2020KLKwangyoung Lee et al.Seoul National UniversityMental Health Apps & Online Support CommunitiesSleep & Stress MonitoringCHI
AILA: Attentive Interactive Labeling Assistant for Document Classification through Attention-Based Deep Neural NetworksDocument labeling is a critical step in building various machine learning applications. However, the step can be time-consuming and arduous, requiring a significant amount of human efforts. To support an efficient document labeling environment, we present a system called Attentive Interactive Labeling Assistant (AILA). In its core, AILA uses Interactive Attention Module (IAM), a novel module that visually highlights words in a document that labelers may pay attention to when labeling a document. IAM utilizes attention-based Deep Neural Networks which not only support a prediction of which words to highlight but also enable labelers to indicate words that should be assigned a high attention weight while labeling to improve the future quality of word prediction.We evaluated the labeling efficiency and the accuracy by comparing the conditions with and without IAM in our study. The results showed that participants' labeling efficiency increased significantly under the condition with IAM than the condition without IAM, while the two conditions maintained roughly the same labeling accuracy.2019MCMinsuk Choi et al.Korea UniversityHuman-LLM CollaborationUser Research Methods (Interviews, Surveys, Observation)CHI
How Do Humans Assess the Credibility on Web Blogs: Qualifying and Verifying Human Factors with Machine LearningThe purpose of this paper is to understand the factors involved when a human judges the credibility of information and to develop a classification model for weblogs, a primary source of information for many people. Considering both computational and human-centered approaches, we conducted a user study designed to consider two cognitive procedures--(1) visceral, behavioral and (2) reflective assessments--in the evaluation of information credibility. The results of the 80-participant study highlight that human cognitive processing varies according to an individual's purpose and that humans consider the structures and styles of content in their reflective assessments. We experimentally proved these findings through the development and analysis of classification models using 16,304 real blog posts written by 2,944 bloggers. Our models yield greater accuracy and efficiency than the models with well-known best features identified in prior research2019YJYonggeol Jo et al.Ajou UniversityAI-Assisted Decision-Making & AutomationAlgorithmic Transparency & AuditabilityCHI