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Applied Sciences
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Social Network Analysis: Opportunities and Challenges
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Social Network Analysis: Opportunities and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 8056

Special Issue Editors

Dr. Kevin Koidl

E-Mail Website
Guest Editor
ADAPT Research Centre, Trinity College Dublin, D02 PN40 Dublin, Ireland
Interests: machine learning (AI); social networks; content engagement; data privacy and ethics
Special Issues, Collections and Topics in MDPI journals
Dr. Barbara Guidi

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, Pisa, Italy
Interests: NFTs; Web3; metaverse; blockchain and cryptocurrencies; decentralized online social networks; peer-to-peer networks; decentralized storages; social network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the opportunities and challenges faced in social network analysis. A focus is placed on empirical (data-based) findings that overcome the current challenges in social network analysis, for example, trust and safety within social networks. This includes content-based approaches, such as ML-based policy-violating content detection, ML-based fake news detection and bullying detection, and structural approaches, such as online social network decentralization, ego network models and blockchain-based approaches.

Dr. Kevin Koidl
Dr. Barbara Guidi
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • social networks as complex networks
  • social network analysis
  • computational social science
  • trust, privacy, and safety in social networks
  • social networks: analysis of applications and new technologies
  • fake news and misinformation in social networks and media
  • AI and machine learning for social networks
  • decentralized solutions for social networks

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Published Papers (2 papers)

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Research

19 pages, 638 KiB  
Article
A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic
by Marcelo Mendoza, Sebastián Valenzuela, Enrique Núñez-Mussa, Fabián Padilla, Eliana Providel, Sebastián Campos, Renato Bassi, Andrea Riquelme, Valeria Aldana and Claudia López
Appl. Sci. 2023, 13(9), 5347; https://doi.org/10.3390/app13095347 - 25 Apr 2023
Cited by 4 | Viewed by 5245
Abstract
Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by [...] Read more.
Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes. Full article
(This article belongs to the Special Issue Social Network Analysis: Opportunities and Challenges)
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17 pages, 2479 KiB  
Article
SEBD: A Stream Evolving Bot Detection Framework with Application of PAC Learning Approach to Maintain Accuracy and Confidence Levels
by Eiman Alothali, Kadhim Hayawi and Hany Alashwal
Appl. Sci. 2023, 13(7), 4443; https://doi.org/10.3390/app13074443 - 31 Mar 2023
Cited by 2 | Viewed by 1976
Abstract
A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be [...] Read more.
A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection (SEBD) framework for Twitter that uses a deep graph neural network. Our SEBD framework was designed based on multi-view graph attention networks using fellowship links and profile features. It integrates Apache Kafka to enable the Twitter API stream and predict the account type after processing. We used a probably approximately correct (PAC) learning framework to evaluate SEBD’s results. Our objective was to maintain the accuracy and confidence levels of our framework to enable successful learning with low misclassification errors. We assessed our framework results via cross-domain evaluation using test holdout, machine learning classifiers, benchmark data, and a baseline tool. The overall results show that SEBD is able to successfully identify bot accounts in a stream-based manner. Using holdout and cross-validation with a random forest classifier, SEBD achieved an accuracy score of 0.97 and an AUC score of 0.98. Our results indicate that bot accounts participate highly in hashtags on Twitter. Full article
(This article belongs to the Special Issue Social Network Analysis: Opportunities and Challenges)
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