Understanding Social Collaborations: In this work , I proposed a novel research design that aims at capturing “real-time” network structures from large-scale news reporting data (~1 million). I characterized the relationship between the two countries by analyzing their news reporting similarity of the APEC (Asia-Pacific Economic Cooperation) Summit in 2013, revealed interesting and meaningful international relations among member countries through network visualizations. In the project [18,25,28,30], I aim to adopt data science approaches to provide future collaboration suggestions. I identified businesses/scholars that might attract the same collaborators with emergent social connections in the future. My works helped to identify the useful features to predict future social collaborations. I conducted offline evaluations to show the prediction model’s high accuracy through supervised learning approaches.
Putting the User in Control: Social recommender system aims to provide useful suggestions to the user and prevent the social overload problem. Most of the research efforts address pushing high relevant items to the top of the ranked list, using a weight ensemble approach. However, I argued that the pre-trained static fusion is insufficient for different specific contexts. In this project [9,10,15,17,26], I developed a series of visual recommendation components and a control panel for the user to interact with the recommendation results at an academic conference. The system offered better recommendation transparency and user-driven fusion of multiple data sources. The experiment result showed that the user did fuse the different recommended sources and exploration patterns among tasks. The user-centric evaluation indicated the effectiveness of the system and the quality of the social explanations. This finding shed light on future research questions about designing a recommender system with human intervention and the interface beyond the static ranked list.
Breaking the Filter Bubble: Increasing diversity in the recommender system's output is an active research question. Most of the current approaches have focused on ranked list optimization to improve recommendation diversity. However, little is known about the effect that a visual interface can have on this issue. My project [9,10,16,20] showed that a multidimensional visualization promotes a diversity of social exploration in academic conferences. I conducted a series of user studies to evaluate my solutions. The study results showed a significant difference in the exploration pattern between the ranking-based and interactive user interface. The results showed that a visual interface could help the user explore a more diverse set of recommended items, which could be used to solve the filter bubble and echo chamber's challenge in the systems.
Investigating Designs for the post-COVID-19 Era: In my recent project [1,5,7], I aim to understand the human decision and experience during the COVID-19 crisis. I have conducted crisis informatics research on government social media, marginalized groups, and the education portioners’ experience during the crisis transition. I used content analysis, inductive thematic analysis, and narrative interfaces to explore the stakeholders’ practice, experience, and perception. My goal is to identify the useful design implications for intelligent systems and sociotechnical systems that could impact human decision-making and collaboration. For instance, public engagement with the government, marginalized community' cross-local collaborations, and new pedagogy for students and faculties.