Special Issue "Application of Big Data for Computational Social Science (ABCSS2019 @ WI 2019)"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (28 February 2020).

Special Issue Editors

Prof. Isamu Okada
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Guest Editor
Department of Business Administration, Soka University, Tokyo 192-8577, Japan
Interests: Evolutionary Game Theory; Social Simulation; Application of Social Dilemma; Computational Social Science
Special Issues and Collections in MDPI journals
Dr. Fujio Toriumi
Website
Guest Editor
Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
Dr. Mitsuo Yoshida
Website
Guest Editor
Department of Computer Science and Engineering, Toyohashi University of Technology, Aichi 441-8580, Japan
Interests: computational social science; natural language processing
Prof. Akira Ishii
Website
Guest Editor
Department of Applied Mathematics and Physics, Tottori University, Koyama, Tottori 680-8552, Japan
Interests: computational social science; sociophysics
Dr. Hiroki Takikawa
Website
Guest Editor
Graduate School of Arts and Letters, Tohoku University, Miyagi, 980-0845, Japan
Interests: computational social science; mathematical sociology

Special Issue Information

Dear Colleagues,

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
Guest Editors

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.

Keywords

  • 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

Published Papers (1 paper)

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Research

Open AccessArticle
Detecting Ethnic Spatial Distribution of Business People Using Machine Learning
Information 2020, 11(4), 197; https://doi.org/10.3390/info11040197 - 07 Apr 2020
Abstract
The development of transportation and technology has spread human movements more quickly and widely. As a result, our societies are becoming more complex, composed of people of more diverse races, cultures, religions, and languages. In this study, we focus on the origins of [...] Read more.
The development of transportation and technology has spread human movements more quickly and widely. As a result, our societies are becoming more complex, composed of people of more diverse races, cultures, religions, and languages. In this study, we focus on the origins of ethnicity while analyzing the background of social members. To track the origin of the ethnicities of which a society is composed, we established a surname-nationality prediction model by learning from a Recurrent Neural Network (RNN) with data recorded by business peoples’ surnames and nationalities to predict nationality with high accuracy through surnames. This study is meaningful because it approaches the social scientific problems of ethnic composition within society through massive data and machine learning: the informatics approach. Full article
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