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 8548

Special Issue Editor

Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
Interests: data science; machine learning; network science

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

Published Papers (5 papers)

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Research

20 pages, 1037 KiB  
Article
Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication
Information 2023, 14(12), 661; https://doi.org/10.3390/info14120661 - 14 Dec 2023
Viewed by 1199
Abstract
Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender’s [...] Read more.
Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender’s social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender’s organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories. Full article
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17 pages, 908 KiB  
Article
An Unsupervised Graph-Based Approach for Detecting Relevant Topics: A Case Study on the Italian Twitter Cohort during the Russia–Ukraine Conflict
Information 2023, 14(6), 330; https://doi.org/10.3390/info14060330 - 12 Jun 2023
Viewed by 1087
Abstract
On 24 February 2022, the invasion of Ukraine by Russian troops began, starting a dramatic conflict. As in all modern conflicts, the battlefield is both real and virtual. Social networks have had peaks in use and many scholars have seen a strong risk [...] Read more.
On 24 February 2022, the invasion of Ukraine by Russian troops began, starting a dramatic conflict. As in all modern conflicts, the battlefield is both real and virtual. Social networks have had peaks in use and many scholars have seen a strong risk of disinformation. In this study, through an unsupervised topic tracking system implemented with Natural Language Processing and graph-based techniques framed within a biological metaphor, the Italian social context is analyzed, in particular, by processing data from Twitter (texts and metadata) captured during the first month of the war. The system, improved if compared to previous versions, has proved to be effective in highlighting the emerging topics, all the main events and any links between them. Full article
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17 pages, 3741 KiB  
Article
Trend Analysis of Decentralized Autonomous Organization Using Big Data Analytics
Information 2023, 14(6), 326; https://doi.org/10.3390/info14060326 - 09 Jun 2023
Cited by 5 | Viewed by 1504
Abstract
Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the [...] Read more.
Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations. Full article
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18 pages, 509 KiB  
Article
Regularized Generalized Logistic Item Response Model
Information 2023, 14(6), 306; https://doi.org/10.3390/info14060306 - 26 May 2023
Cited by 2 | Viewed by 1109
Abstract
Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous [...] Read more.
Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous and polytomous items using the asymmetric generalistic logistic link function that covers a lot of symmetric and asymmetric link functions. Compared to IRT modeling based on the logistic or probit link function, the generalized logistic link function additionally estimates two parameters related to the asymmetry of the link function. To stabilize the estimation of item-specific asymmetry parameters, regularized estimation is employed. The usefulness of the proposed model is illustrated through simulations and empirical examples for dichotomous and polytomous item responses. Full article
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20 pages, 2509 KiB  
Article
Mapping Thriving at Work as a Growing Concept: Review and Directions for Future Studies
Information 2022, 13(8), 383; https://doi.org/10.3390/info13080383 - 10 Aug 2022
Cited by 10 | Viewed by 2651
Abstract
This study aims to provide a bibliometric analysis of the literature on thriving at work in psychology and business/management produced between 2001 and 2021, using the Web of Science (WoS) database. The analyses allowed us to identify, through 190 documents, the emergence of [...] Read more.
This study aims to provide a bibliometric analysis of the literature on thriving at work in psychology and business/management produced between 2001 and 2021, using the Web of Science (WoS) database. The analyses allowed us to identify, through 190 documents, the emergence of the concept of thriving at work and its development. The main research variables related to this concept and its methodology were identified. Likewise, the most influential authors, the most cited articles, the more frequently cited journals, and the countries contributing to developing this construct are analyzed. In addition, an analysis of co-citation, co-occurrences, and bibliographic coupling was conducted. Finally, content analysis of the most popular keywords and the co-citation of cited references are conducted. These analyses allow the identification of the main developments in the topic of thriving at work. The theoretical and practical implications of this bibliometric analysis are discussed. Full article
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