Summer Institute in Computational Social Science

계산사회과학여름학교
  • Time and Date :

    Jun 19 (Sun) – Jun 29 (Wed), 2022

  • Venue :

    KAIST Graduate School of Culture Technology Building, Paik Nam June Hall

  • Organizing Committee :

    Ran Wu Kim (KAIST School of Digital Humanities and Computational Social Sciences), Jae Yeon Kim (KDI School), Won Jae Lee (KAIST Graduate School of Culture Technology)

  • Participants :

    A total of 18 participants from 14 universities home and abroad, mainly consisting of students in the Ph.D. programs and professors.

  • Program Highlight
    • SICSS Program Practicum and Discussion

      Practicum and discussions on topics that have to be considered by the researchers in the computational social sciences, including ethical standards, big data approaches, implications and limitations of online surveys, online experiments, and understanding mass collaboration research. Refer to schedule.

      Refer to schedule
    • Invitational Lectures

      • “Computational approaches of studying (political) communication within social contexts: opportunities, challenges, and pitfalls” (Hyun-Jin Song, Assistant Professor at School of Communications, Yonsei University)

      • “Understanding China in the world through quantitative social science” (Sung-Eun Kim, Associate Professor, School of Politics, Korea University)

      • “Introduction to Twitter Academic API 2.0 and User Guides” (Suhem Parack, development engineer at Twitter)

      • Introduction to how computational social science approaches are applied in US civil societies (Eric Giannella, Code for America)

      • Introduction to the social background for innovations and findings on pattern research (Hye-Jin Youn, Northwestern University, associate professor)

      • “Causal inference: Born in Social Sciences, Applied to Big Tech Problems” (Jin-Young Kim, Data Science Analytics Team, Naver)

    • Panel Sessions
      • Korea Data Panel (Jun 22, Wednesday, 12:30)
        • a. Panelists:

          In-Bok Lee (KDI School), Jae-Sung Choi (Sungkyunkwan University), Jae-yeon Kim (KDI School).

        • b.

          Introduced how the public data infrastructure is established to study Korea and how and where to access it.

      • Asia+Pacific Joint Panel: Hidden career path (Jun 24, Friday, 09:00)
        • a. Panelists:

          Lanu Kim (host), Jae-yeon Kim (KDI School), Cecilia Liu (Law), Tiago Ventura (Georgetown University)

        • b.

          Invited individuals with unusual careers working in the computational social sciences sector as panelists and listened to their experiences. The focus of the panel was to show that the careers from the field are not limited to academics but cover diverse areas, to help other participants in their career search.

    • Group Projects
      • 6/20 – Group project pitching

      • 6/21 – Research speed dating + group formulation

      • 6/22 – Scoping the project

      • 6/23 – Data collection

      • 6/24 – Preliminary data analysis

      • 6/27 – Data analysis

      • 6/28 – Data communication

  • Participant Feedback

    • Carried out a survey at the end of the program to hear about the overall experience of the camp :
      91% of the respondents said they plan to continue collaboration with researchers of other disciplines. Also, 75% of the respondents replied that the camp experience was very positive.

    • “The learnings from the summer camp were very practical, including programming in R and web scraping, and the invitational lectures helped me widen my understanding about the research fields of computational social sciences.”

    • Moreover, the participants fully leveraged the informal networking opportunities in addition to the formal events, and showed a general level of satisfaction towards the career advice.

    • Lastly, the level of interest and understanding on inter-disciplinary research were enhanced while continuously conversing with other participants, organizers, and invitational speakers with diverse academic backgrounds.

    • Many of the respondents revealed their plans to continue learning the CSS approaches after the camp, with particularly higher interest in machine learning or text analysis.

    • Respondents showed high levels of satisfaction for the camp operations, and some wished that the camp will continue next year.