[AISWorld] AMCIS 2024 CFP for Computational Social Science Research through Analytics

[Vo, Ace]

Dear Colleagues:

Please consider submitting a manuscript to the Computational Social Science Research through Analytics minitrack under the AMCIS 2025 Data Science and Analytics in the Decision Support Track.

Computational social science research has garnered much interest from multiple disciplines through the use of massive, multi-faceted, and authentic trace data. A recent trend in understanding social phenomena using computational social science research, especially using analytics, has led to many discoveries of, and confirmation of hypotheses and theories. The interdisciplinary nature of computational social science research is suitable for our field, for Information Systems can demonstrate both rigor and relevance of answering social science questions through innovative use of data analytics. As a discipline, Information Systems enables the collection, processing, and analyzing trace data, which are event-based records of activities of transactions that could be found in systems across organizations and the Internet. Therefore, our field is poised to explicate interesting and valuable insights.

Thanks to the implosion in data analytics tools such as data mining, machine learning, artificial intelligence, researchers can augment understanding of existing problems and elucidate current perplexing issues. Large-scale problems are no longer hard to reach, but interesting and with a plethora of research directions. In general, the guideline for computational research has percolated through various disciplines via leading research outlets like Nature, Information Systems Research, Management Information Systems Quarterly, and Communications of the ACM have started to solicit calls in this nascent research field to attract more researchers to work on interesting problems and theory building from data.

This minitrack encourages research on the utilization of data to explore, and potentially answer social phenomena. Submissions may focus on descriptive research processes, novel algorithm designs, questions forming, new and interesting directions in computational social science. In addition, nascent theory forming through a bottom-up approach using data is especially encouraged. Research in any domains is welcome, including but not pertaining only to, persuasion, ethics, equality, social benefit distribution, and humanitarian efforts.

Below is a list of recommended topics, however, other relevant topics are also welcome:


  *   Algorithm designs in Computational Social Science
  *   Computational Social Science strategies and research processes
  *   The role of Information Systems in Computational Social Science
  *   Computational Social Science interdisciplinary research
  *   Computational Social Science with Big Data applications
  *   Computational Social Science in changing and/or influencing human behaviors
  *   Ethics of Computational Social Science research on human behaviors
  *   Nascent theory and hypothesis forming using data (quantitative grounded theory)
  *   Integration of Generative AI tools in Computational Social Science research
  *   Testing and implementing Generative AI tool as a possible solution for Computational Social Science


Here are the important dates:

  *   January 5, 2025: Manuscript submissions begin
  *   February 28, 2025: Submissions are due at 5:00PM EST
  *   May 9, 2025: TREOs, PDS and Panels submissions are due at 5:00PM EST


(Berente, Seidel, & Safadi, 2018; Conte et al., 2012; Giles, 2012; Rai, 2016; Wallach, 2018)
Reference
  Berente, N., Seidel, S., & Safadi, H. (2018). Research Commentary—Data-Driven Computationally Intensive Theory Development. Information Systems Research, 30(1), 50–64.
Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., … Helbing, D. (2012). Manifesto of computational social science. The European Physical Journal Special Topics, 214(1), 325–346. https://doi.org/10.1140/epjst/e2012-01697-8
Giles, J. (2012). Computational social science: Making the links. Nature News, 488(7412), 448. https://doi.org/10.1038/488448a
Rai, A. (2016). Editor’s comments: Synergies between big data and theory. MIS Quarterly, 40(2), iii–ix.
Wallach, H. (2018). Computational Social Science ≠ Computer Science + Social Data. Communications of the ACM, 61(3), 42–44. https://doi.org/10.1145/3132698

Thank you for your consideration,



Best,

Ace Vo (Loyola Marymount University, CA, USA - ace.vo at lmu.edu)

Yan Li (Claremont Graduate University, CA, USA - yan.li at cgu.edu )

     Anitha Chennamaneni, Ph.D. (Texas A & M University Central, Texas, USA -
anitha.chennamaneni at tamuct.edu<mailto:anitha.chennamaneni at tamuct.edu>)

Miloslava Plachkinova (Kennesaw State University, Kennesaw, GA, USA - mplachki at kennesaw.edu<mailto:mplachki at kennesaw.edu>)




Best,

-Ace
Ace Vo, Ph.D., P.M.P.

Associate Professor
Dept of Information Systems & Business Analytics
College of Business Administration
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