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Computers
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Recent Advances in Social Networks and Social Media
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Recent Advances in Social Networks and Social Media

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 8691

Special Issue Editor

Prof. Dr. Hovhannes Harutyunyan

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: combinatorics of networks; algorithmic graph theory; parallel and distributed algorithms

Special Issue Information

Dear Colleagues,

Studies of social networks have been conducted for a century in a variety of disciplines such as sociology, psychology, economics, and anthropology. The recent advances in Internet, the social web, and other large-scale social and technological infrastructure have triggered a growing interest and significant methodological advancements in social network analysis and mining. Inspired by these research problems, new methods from graph theory, machine learning and data mining, statistics, and statistical mechanics have been developed, which in turn opens up further possibilities for more interesting applications. These have led to a rising prominence of analysis of social networks and social media using different methods and tools from academia, business, and politics.

We are excited to announce a Special Issue of the journal Computers on the topic of "Recent Advances in Social Networks and Social Media". This Special Issue aims to bring together the latest research on recent developments in social networks and social media. We welcome novel research articles, comprehensive reviews, and survey articles. Extended conference papers are also welcome. They should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

Topics of interest include, but are not limited to:

  • network formation;
  • social networks;
  • network experiments;
  • local interaction games;
  • non-cooperative games;
  • real-world complex networks analysis;
  • pattern analysis in social networks;
  • temporal networks;
  • information diffusion models;
  • reputation and trust in social media;
  • social influence, recommendation, and media.

Prof. Dr. Hovhannes Harutyunyan
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. Computers 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

  • network formation
  • social networks
  • network experiments
  • local interaction games
  • non-cooperative games
  • real-world complex networks analysis
  • pattern analysis in social networks
  • temporal networks
  • information diffusion models
  • reputation and trust in social media
  • social influence, recommendation, and media

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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40 pages, 21233 KiB  
Article
Large-Scale Cross-Cultural Tourism Analytics: Integrating Transformer-Based Text Mining and Network Analysis
by Dian Puteri Ramadhani, Andry Alamsyah, Mochamad Yudha Febrianta, Muhammad Nadhif Fajriananda, Mahira Shafiya Nada and Fathiyyah Hasanah
Computers 2025, 14(1), 27; https://doi.org/10.3390/computers14010027 - 16 Jan 2025
Viewed by 637
Abstract
The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based [...] Read more.
The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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35 pages, 9938 KiB  
Article
Detection of Fake Instagram Accounts via Machine Learning Techniques
by Stefanos Chelas, George Routis and Ioanna Roussaki
Computers 2024, 13(11), 296; https://doi.org/10.3390/computers13110296 - 15 Nov 2024
Viewed by 1894
Abstract
This paper focuses on the detection of fake accounts on Instagram and proposes a novel solution that aims to address this problem. More specifically, a machine learning-based solution is introduced that can be employed by Instagram-based applications to combat this phenomenon. To accomplish [...] Read more.
This paper focuses on the detection of fake accounts on Instagram and proposes a novel solution that aims to address this problem. More specifically, a machine learning-based solution is introduced that can be employed by Instagram-based applications to combat this phenomenon. To accomplish this, publicly available data from Instagram users are collected and processed. After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. The interest of the results presented in this study lies in their comparability with the results of other similar studies, with the difference that fewer input features have been used. This addresses significant research challenges regarding the performance of and confidence in obtained results in this specific problem domain. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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29 pages, 3031 KiB  
Article
Technical Innovations and Social Implications: Mapping Global Research Focus in AI, Blockchain, Cybersecurity, and Privacy
by Emanuela Bran, Răzvan Rughiniș, Dinu Țurcanu and Gheorghe Nadoleanu
Computers 2024, 13(10), 254; https://doi.org/10.3390/computers13100254 - 8 Oct 2024
Cited by 1 | Viewed by 2448
Abstract
This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus [...] Read more.
This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus and cluster analysis to reveal patterns across countries, combining these methods to provide a broader view of global research priorities. Our findings reveal that liberal democracy index, life expectancy, and happiness are significant predictors of research focus, while traditional indicators like education and income show weaker relationships. This unexpected result challenges conventional assumptions about the drivers of research priorities in digital technologies. The study identifies distinct clusters of countries with similar patterns of research focus across the four technologies, revealing previously unrecognized global typologies. Notably, more democratic societies tend to emphasize social implications of technologies, while some rapidly developing countries focus more on technical aspects. These findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how we understand and predict research focus in rapidly evolving technological fields. The study provides valuable information for policymakers and researchers, informing strategies for technological development and international collaboration in an increasingly digital world. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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13 pages, 3310 KiB  
Article
Dynamic Opinion Formation in Networks: A Multi-Issue and Evidence-Based Approach
by Joel Weijia Lai
Computers 2024, 13(8), 190; https://doi.org/10.3390/computers13080190 - 7 Aug 2024
Viewed by 1427
Abstract
In this study, we present a computational model for simulating opinion dynamics within social networks, incorporating cognitive and social psychological principles such as homophily, confirmation bias, and selective exposure. We enhance our model using Dempster–Shafer theory to address uncertainties in belief updating. Mathematical [...] Read more.
In this study, we present a computational model for simulating opinion dynamics within social networks, incorporating cognitive and social psychological principles such as homophily, confirmation bias, and selective exposure. We enhance our model using Dempster–Shafer theory to address uncertainties in belief updating. Mathematical formalism and simulations were performed to derive empirical results from showcasing how this method might be useful for modeling real-world opinion consensus and fragmentation. By constructing a scale-free network, we assign initial opinions and iteratively update them based on neighbor influences and belief masses. Lastly, we examine how the presence of “truth” nodes with high connectivity, used to simulate the influence of objective truth on the network, alters opinions. Our simulations reveal insights into the formation of opinion clusters, the role of cognitive biases, and the impact of uncertainty on belief evolution, providing a robust framework for understanding complex opinion dynamics in social systems. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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20 pages, 298 KiB  
Article
Teachers’ Needs for Support during Emergency Remote Teaching in Greek Schools: Role of Social Networks
by Stefanos Nikiforos, Eleftheria Anastasopoulou, Athina Pappa, Spyros Tzanavaris and Katia Lida Kermanidis
Computers 2024, 13(7), 177; https://doi.org/10.3390/computers13070177 - 18 Jul 2024
Viewed by 1166
Abstract
The onset of the COVID-19 pandemic prompted a rapid shift to Emergency Remote Teaching (ERT). Social networks had a key role in supporting the educational community in facing challenges and opportunities. A quantitative study was conducted to assess the Greek teachers’ perceptions of [...] Read more.
The onset of the COVID-19 pandemic prompted a rapid shift to Emergency Remote Teaching (ERT). Social networks had a key role in supporting the educational community in facing challenges and opportunities. A quantitative study was conducted to assess the Greek teachers’ perceptions of social network support. Findings indicated that teachers turned to universities, educational institutions, the Ministry of Education, school support groups, and virtual communities for support. Additionally, the study revealed the barriers faced by teachers, including infrastructure limitations, technical difficulties, skill deficiencies, problems with students’ engagement, and school policies. Teachers’ evaluation of support regarding ERT provided fruitful insight. The results illustrate teachers’ perspectives on ERT, contributing to the ongoing discourse on educational resilience to unpredictable disruptions. In conclusion, the role of social networks was considered as critical for the teachers to overcome barriers during ERT with the formation of social communities for support and the sharing of common experiences. Expertise in internet use and social networking played a significant role in readiness for the abrupt shift to distance education. The present study uniquely contributes to the educational field by emphasizing the role of teachers’ support as an innovative approach to holistically enhance teachers’ performance in ERT. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)

Review

Jump to: Research

19 pages, 529 KiB  
Review
Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond
by Harshit Srivastava and Ravi Sankar
Computers 2025, 14(2), 42; https://doi.org/10.3390/computers14020042 - 28 Jan 2025
Viewed by 438
Abstract
X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed [...] Read more.
X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed and the complexity of finding meaningful patterns and events. These data streams are generated in multiple formats, with constant updating, and are real-time in nature; thus, they require sophisticated algorithms capable of dynamic event detection in this dynamic environment. Event detection techniques have recently achieved substantial development, but most research carried out so far evaluates only single methods, not comparing the overall performance of these methods across multiple platforms and types of data. With that view, this paper represents a deep investigation of complex state-of-the-art event detection algorithms specifically customized for streams of data from X. We review various current techniques based on a thorough comparative performance test and point to problems inherently related to the detection of patterns in high-velocity streams with noise. We introduce some novelty to this research area, supported by appropriate robust experimental frameworks, to performed comparisons quantitatively and qualitatively. We provide insight into how those algorithms perform under varying conditions by defining a set of clear, measurable metrics. Our findings contribute new knowledge that will help inform future research into the improvement of event detection systems for dynamic data streams and enhance their capabilities for real-time and actionable insights. This paper will go a step further than the present knowledge of event detection and discuss how algorithms can be adapted and refined in view of the emerging demands imposed by data streams. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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