Browsing by Author "Yu, Xing"

Now showing 1 - 4 of 4
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    A Co-Training Model with Label Propagation on a Bipartite Graph to Identify Online Users with Disabilities
    (AAAI, 2019) Yu, Xing; Chakraborty, Sunandan; Brady, Erin; Human-Centered Computing, School of Informatics and Computing
    Collecting data from representative users with disabilities for accessibility research is time and resource consuming. With the proliferation of social media websites, many online spaces have emerged for people with disabilities. The information accumulated in such places is of great value for data collection and participant recruiting. However, there are also many active non-representative users in such online spaces such as medical practitioners, caretakers, or family members. In this work, we introduce a novel co-training model based on the homophily phenomenon observed among online users with the same disability. The model combines a variational label propagation algorithm and a naive Bayes classifier to identify online users who have the same disability. We evaluated this model on a dataset collected from Reddit and the results show improvements over traditional models.
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    Designing Leaderboards for Gamification: Perceived Differences Based on User Ranking, Application Domain, and Personality Traits
    (ACM, 2017-05) Jia, Yuan; Liu, Yikun; Yu, Xing; Voida, Stephen; Human-Centered Computing, School of Informatics and Computing
    Leaderboards, a common gamification technique, are used to enhance engagement through social comparisons. Prior research has demonstrated the overall utility of leaderboards but has not examined their effectiveness when individuals are ranked at particular levels or when the technique is applied in different application domains, such as social networking, fitness, or productivity. In this paper, we present a survey study investigating how preferences for leaderboards change based on individual differences (personality traits), ranking, social scoping, and application domains. Our results show that a respondent's position on the leaderboard had important effects on their perception of the leaderboard and the surrounding app, and that participants rated leaderboards most favorably in fitness apps and least favorably in social networking contexts. More extraverted people reported more positive experiences with leaderboards despite their ranking or the application domain. We present design implications for creating leaderboards targeted at different domains and for different audiences.
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    Gestchat (A Tool to Support Emotional Communication in Text Messaging)
    (Office of the Vice Chancellor for Research, 2015-04-17) Pirzadeh, Afarin; Donnelly, Kaelyn; Elbin, Julie; Lin, Ann Marie May; Yu, Xing
    We introduce a new mobile instant messaging application, which specifically support users’ emotional communication. The invention relates to a novel method in instant messaging to input interactive emoticons through multi-touch gestures. We add additional mode of gesture to instant messaging keyboard and also send text in different colors to present the emotion.
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    Using Social Media Websites to Support Scenario-Based Design of Assistive Technology
    (2020-01) Yu, Xing; Brady, Erin; Palakal, Mathew; Bolchini, Davide; Chakraborty, Sunandan; Hasan, Mohammad
    Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies.