Advances in Data and Network Sciences Applied to Computational Social Science
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 11715
Special Issue Editor
Special Issue Information
Dear Colleagues,
The MDPI journal Information invites submissions for a Special Issue on “Advances in Data and Network Sciences Applied to Computational Social Science”.
Computational social science (CSS) is a research area devoted to the study of social phenomena represented by digital data using computational and statistical methods. CSS emerged after a computational revolution in social sciences caused by two main factors. First, new large-scale datasets allowed the study of human behavior that would not be possible using traditional methodological approaches used by social scientists (e.g., surveys and lab experiments). These datasets come from different sources such as social media, mobile phones, satellites, surveillance cameras, and all sorts of sensors. Second, faster computers and new computational techniques permitted the extraction of information from these huge behavioral datasets. Most of these techniques come from data and network sciences—two research areas in constant evolution and with many open questions. Some examples include complex data modeling, model selection in complex tasks, data biases, fairness, and forecasting. CSS also has many unanswered questions involving predictability, long-term impact, causality, interpretability, privacy, and ethics.
This Special Issue is dedicated to the development of new methods of data and network sciences applied to computational social science. Topics include (but are not limited to):
- Supervised learning;
- Unsupervised learning;
- Deep learning;
- Graph neural networks;
- Time series data mining;
- Text analysis and natural language processing (NLP);
- Spatiotemporal data mining;
- Forecasting;
- Network analysis;
- Community detection;
- Temporal networks;
- Epidemics in networks;
- Causal inference;
- Social networks analysis;
- Social media studies;
- Simulations of social phenomena;
- Large-scale social experiments.
Complete instructions for authors can be found at: https://www.mdpi.com/journal/information/instructions
Dr. Leonardo Nascimento Ferreira
Guest Editor
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. 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 1600 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
- data science
- network science
- machine learning
