Special Issue "Network analysis and computational social science: theory, methods, applications, future perspectives"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editors

Dr. Nicola Lettieri
Website SciProfiles
Guest Editor
1. INAPP - National Institute for Public Policies Analysis, C.so d'Italia 33, 00198 - Rome, Italy
2. Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Piazza Arechi II, 82100 - Benevento, Italy
Interests: law and computational social science; complexity-inspired approaches to law and policy making; computational legal empiricism, agent-based social simulation; social network analysis; visual legal analytics; techno-regulation, gamification and legal education
Special Issues and Collections in MDPI journals
Prof. Delfina Malandrino
Website
Guest Editor
Università di Salerno, Salerno, Italy
Interests: distributed systems on the World Wide Web and intermediaries, collaborative and learning systems, social and network analysis, privacy, green computing and power-aware software, usability studies, visualization, computational social science, computational legal studies, techno-regulation
Special Issues and Collections in MDPI journals
Prof. Dr. Giancarlo Ruffo
Website SciProfiles
Guest Editor
Department of Computer Science, University of Turin, 10121 Turin, Italy
Interests: computational social science; complex networks; network science; network analysis; data science; social media analysis
Special Issues and Collections in MDPI journals
Dr. Rocco Zaccagnino
Website
Guest Editor
Università di Salerno, Salerno, Italy
Interests: computational intelligence: theory and applications, computer music, formal languages, bioinformatics, evolutionary computing, computational social science, computational legal studies, techno-regulation
Dr. Alessandra Sala
Website
Guest Editor
Nokia Bell Labs, Dublin, Ireland
Interests: Machine Learning, Graph Theory, Mathematics of Networks, Statistical & Data Sciences

Special Issue Information

Dear Colleagues,

As witnessed by a growing multidisciplinary literature, network science is today a flourishing area whose heuristics are pervading extremely different fields. The computational investigation of networks’ features has seeped into the study of complex and interdependent phenomena, widening the methodological apparatus in disciplines spanning from physics to biochemistry, and from genetics to neuroscience.

Over the last ten years, complex network analysis (CNA) has played a crucial role in understanding network processes and human behaviors at a local and global scale. Today there is a growing understanding that CNA’s computational models and algorithms would be able to shed light on complex and dynamic behaviors previously inaccessible in the most diverse areas of social science from sociology to law, anthropology or political science.

This call for paper aims to explore the theoretical, methodological, and applicative level aspects of the interplay between social science and CNA to pursue a deeper understanding of social science research issues via network-based inferences. The ultimate goal is to promote an interdisciplinary debate and opportunity for cross-fertilization among research areas about current and future use of CNA in computational social science. In line with the Future Internet scope, special attention will be placed on research connected to web technologies (web applications, social media analysis). Contributions, either theoretical or applied, are welcome. Below is a (non-exhaustive) list of potential topics:

  • Innovative applications of CNA in social science (law, economics, criminology, political science etc.)
  • Algorithms for CNA
  • CNA and Social Media
  • CNA and visual analytics
  • Bio-inspired approaches to CNA in computational social science
  • Integration of CNA with other computational heuristics (machine learning, agent-based modeling, data mining, genetic algorithms, natural language processing)
  • Multi-level CNA
  • Interplay between structural features and opinion dynamics
  • Trends on algorithmic personalization and individual versus collective behaviors
  • Misinformation diffusion and impact on society
  • Language and networks: debates, echo-chambers, and segregated communities
  • Innovation diffusion and prediction
  • Science of Science
  • Tools and technologies for CNA

Dr. Nicola Lettieri
Prof. Delfina Malandrino
Prof. Giancarlo Ruffo
Dr. Rocco Zaccagnino
Dr. Alessandra Sala
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. Future Internet 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.

Published Papers (1 paper)

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Research

Open AccessArticle
Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate
Future Internet 2020, 12(10), 173; https://doi.org/10.3390/fi12100173 - 15 Oct 2020
Abstract
In this work, we apply network science to analyse almost 6 M tweets about the debate around immigration in Italy, collected between 2018 and 2019, when many related events captured media outlets’ attention. Our aim was to better understand the dynamics underlying the [...] Read more.
In this work, we apply network science to analyse almost 6 M tweets about the debate around immigration in Italy, collected between 2018 and 2019, when many related events captured media outlets’ attention. Our aim was to better understand the dynamics underlying the interactions on social media on such a delicate and divisive topic, which are the actors that are leading the discussion, and whose messages have the highest chance to reach out the majority of the accounts that are following the debate. The debate on Twitter is represented with networks; we provide a characterisation of the main clusters by looking at the highest in-degree nodes in each one and by analysing the text of the tweets of all the users. We find a strongly segregated network which shows an explicit interplay with the Italian political and social landscape, that however seems to be disconnected from the actual geographical distribution and relocation of migrants. In addition, quite surprisingly, the influencers and political leaders that apparently lead the debate, do not necessarily belong to the clusters that include the majority of nodes: we find evidence of the existence of a ‘silent majority’ that is more connected to accounts who expose a more positive stance toward migrants, while leaders whose stance is negative attract apparently more attention. Finally, we see that the community structure clearly affects the diffusion of content (URLs) by identifying the presence of both local and global trends of diffusion, and that communities tend to display segregation regardless of their political and cultural background. In particular, we observe that messages that spread widely in the two largest clusters, whose most popular members are also notoriously at the opposite sides of the political spectrum, have a very low chance to get visibility into other clusters. Full article
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