Special Issue "Application of Big Data for Computational Social Science (ABCSS2019 @ WI 2019)"
A special issue of Information (ISSN 2078-2489).
Deadline for manuscript submissions: 28 February 2020.
Contemporary social sciences are facing a serious paradigm shift because of the developments in computer and Internet technologies, though traditional social sciences are still very important. Big data, such as digital traces of online activities and mobility records, allow us to quantify human behavior and social phenomena at a fine-grained level, yet they are global in scale, thereby complementing experimental data and theoretical and computational simulation results. In some cases, we can even employ the methods of natural sciences, including physics, chemistry or biology, in order to analyze big data. From this perspective, we are organizing the workshop ABCSS2019 @ WI2019 of “Applications of Big Data for Computational Social Science”. The scopes of the workshop include the applications of big data, as well as the methods for collecting and using big data for computational social science. Moreover, theoretical frameworks and computational techniques for big data are also very important topics in our workshop. In this workshop, social sciences are not limited to sociology, economics, marketing, and political science but also include informatics, complexity science, econophysics, sociophysics, culturomics, and the arts.
Selected papers which were presented at [email protected] are invited to be submitted as extended versions to this Special Issue of the journal Information. The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Information and collected together on this Special Issue website.
Prof. Isamu Okada
Dr. Fujio Toriumi
Dr. Mitsuo Yoshida
Prof. Akira Ishii
Dr. Hiroki Takikawa
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Application of sociology/sociophysics using big data
- Application of econometric/econophysics using big data
- Social media data analyses from economic/political/social perspective
- Informatics using social big data
- Marketing science using social big data
- Business analytics using big data on consumer behavior
- Culturomics and art management
- Analysis of reputation of entertainment using big data