-------- Forwarded Message -------- Subject: [AISWorld] AMIS 2020 CFP - Computational Social Science Research through Analytics minitrack Date: Sun, 05 Jan 2020 15:20:54 -0800 From: Au Vo professor.auvo@gmail.com To: This is the AISWorld List Server aisworld@lists.aisnet.org
Dear Colleagues:
Please consider submitting a manuscript to the Computational Social Science Research through Analytics minitrack under the AMCIS 2020 Data Science and Analytics for Decision Support Track. AMCIS 2020 will take place in Salt Lake City, Utah on August 12-16. The online submission system opens on January 6 and closes on February 28. Following is a short description of our minitrack and if you have any other questions, do not hesitate to contact us. We look forward to seeing you in Salt Lake City in 2020.
Computational social science research has garnered much interest from multiple disciplines through the use of massive, multi-faceted, and authentic data. A recent trend in understanding social phenomena using computational social science research, especially through the use of 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 has the ability to demonstrate both rigor and relevance of answering social science questions through innovative use of data analytics. As a discipline, Information Systems enable 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 are poised to explicate interesting and valuable insights.
Thanks to the implosion in data analytics tools such as data mining, machine learning, artificial intelligence, researchers have the ability to augment understanding of existing problems and elucidate current perplexing issues. Large-scale problems are no longer a hard-to-reach problem, but an interesting one 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 process, 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 are 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 through the use of data (quantitative grounded theory) (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 [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 [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 [https://doi.org/10.1145/3132698]
Thank you for your consideration,
Best,
Au Vo (Loyola Marymount University, CA, USA - au.vo@lmu.edu [mailto:au.vo@lmu.edu])
Yan Li (Claremont Graduate University, CA, USA - yan.li@cgu.edu [mailto:yan.li@cgu.edu] )
Anitha Chennamaneni (Texas A & M University Central, Texas, USA - anitha.chennamaneni@tamuct.edu [mailto:anitha.chennamaneni@tamuct.edu]) _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org