Computational Social Science and Complex Systems
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 26770
Special Issue Editors
Interests: complex systems; network science; systems biology and computational social science
Interests: complex systems; non-equilibrium physics; networks; biophysics; math modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Social and technological revolutions, such as the internet and social media, have profoundly transformed the way humans interact with one another, leading to the development of disciplines such as computing and information technology. We now have access to previously unimaginable amounts of information and high-resolution dynamical data that other sciences can only imagine. From the movements of individuals to the continuous activity taking place in social networks, a challenge for the social and computational sciences is to extract relevant information from these massive amounts of data and unravel the mechanisms that drive its complex dynamics. Computational social science is an emerging discipline in charge of developing and applying computational methods to deal with complex, large-scale, human behavioral data. This interdisciplinary field has attracted great interest among not only social scientists, but also among computer scientists and statistical physicists alike.
This Special Issue is devoted to presenting recent developments in the computational and mathematical techniques of data extraction and visualization, analysis, and modeling of complex social structures, and bringing a new understanding to the field of computational social sciences. The topics of this Special Issue include but are not limited to:
- Computer simulation applications in social systems;
- Social media and social network analysis;
- Application of big data and artificial intelligence in social science;
- Social math and modeling;
- Progress of complex systems;
- Computational modeling of cognition;
- Ethics and computational social science.
Dr. Minzhang Zheng
Dr. Pedro D. Manrique
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 submissions that pass pre-check are 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. Computation 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 1800 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
- computational social science
- complex systems
- big data
- social networks
- machine learning
- natural language processing.
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Multiplex Network Analysis of Inter-party Relationships
Authors: Harun Pirim; Yunus Orhan; Yusuf Akbulut
Affiliation: North Dakota State University, Fargo, United States
Abstract: In polarized societies, inter-party interactions between candidates can often lead to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between candidates. Here we study inter-party interactions across 6000 US 2022 midterm election candidates on Twitter, examining (a) which topics spark the most heated debates within candidate networks on Twitter and (b) how toxicity level/gender/race of candidates moderate the inter-party network interactions. We expect that (1) most of the links originating within either the Democrat or Republican candidates could stay within their party communities; (2) this pattern could be more evident in the retweet network, but less in the mention network; (3) inter-party interactions could be more frequent at the district-level; and (4) polarization around specific policy issues (i.e., gun ownership, abortion, LGBT) could be more relevant to explain the inter-party network.
We will deploy multiplex network analysis to address these questions and derive new hypotheses. Multiplex network analysis represents distinct relationships at each layer. We will select at least five hashtags to create at least five layers of the multiplex network. The layers will be compared in terms of network metrics such as centrality, average path length, density, and clustering coefficient. Community detection will be applied to see how the candidates are clustered, considering all layers of relations. To elucidate toxicity, gender, and race relationships three layers of multiplex network will be generated. The layers will be compared, and the multiplex network will be clustered to reveal bipartisan relationships. Another multiplex network will be based on retweet and mentions networks. This network will be analyzed to see if there are party level vs. district level distinctions. Our substantive finding could suggest that using less toxic language could be a protective factor that enhances inter-party network effectiveness.