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Dear Colleagues:
Please consider submitting a manuscript to the Computational
Social Science Research in Information Systems 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)
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,
Au Vo (Loyola Marymount University, CA, USA -
au.vo@lmu.edu)
Yan Li (Claremont Graduate University, CA, USA -
yan.li@cgu.edu )
Anitha Chennamaneni (Texas A & M University Central, Texas,
USA -
anitha.chennamaneni@tamuct.edu)
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