Topic Editors

Prof. Dr. Carson K. Leung
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Dr. Fei Hao
School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
Prof. Dr. Giancarlo Fortino
Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Dr. Xiaokang Zhou
Faculty of Data Science, Shiga University, Kyoto 520-0002, Japan

Social Computing and Social Network Analysis

Abstract submission deadline
30 September 2023
Manuscript submission deadline
31 December 2023
Viewed by
1192

Topic Information

Dear Colleagues,

Social networks in the physical world have long been studied in various disciplines such as anthropology, economics, psychology, and sociology. With technological advances, social networks have become popular in the cyber world with the growth of the Internet, social web, and social network sites. Consequently, social computing (SoC) and social network analysis (SNA) have drawn the interest of researchers and practitioners in computational sciences and related disciplines. The SoC examines collaborative, interactive, and social behavior among people, and the SNA investigates and analyzes social structures through the use of network, graph theory, data mining, machine learning, and statistics. The topic invites submissions on theoretical and practical issues on social computing and social network analysis (SoC and SNA), including but not limited to:

  • The fundamentals of social computing;
  • Theories for social networks analysis;
  • Modeling social media;
  • Data mining for social media data;
  • Communities mining in social media;
  • Expert systems for social media data;
  • Recommendation systems and marketing;
  • Trust and reputation evaluation in (mobile) social networks;
  • Methods for social structure and community discovery;
  • Methods for tie strength or link prediction;
  • Methods for extracting and understanding user and group behavior;
  • Big social media data;
  • Social computing and network analysis techniques (e.g., social data collection, quality, scalability);
  • Social computing and network analysis problems (e.g., centrality, roles, community detection, link prediction, information diffusion, influence propagation, anomaly detection, privacy and security, collective behavior, crowd sourcing, social recommenders, misinformation and misbehavior detection and analysis);
  • Trustworthy social network (e.g., reputation and trust in social networks, responsible social network analysis, fairness bias, and transparency in social media);
  • Explainable social network analysis;
  • Other issues related to the advances of social computing;
  • Social computing and network analysis applications and case studies (e.g., attributed, online/offline, probabilistic, semantics, time-evolving social networks).

The topic focuses on one theme—namely, social computing and social network analysis (SoC and SNA). However, it provides authors with multiple choices of venues—namely, five different journals.

Prof. Dr. Carson K. Leung
Dr. Fei Hao
Prof. Dr. Giancarlo Fortino
Dr. Xiaokang Zhou
Topic Editors

Keywords

  • social computing
  • social network analysis
  • social media
  • social networks
  • information propagation
  • social sensing
  • internet

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.838 3.7 2011 14.9 Days 2300 CHF Submit
Big Data and Cognitive Computing
BDCC
- 6.1 2017 17.2 Days 1600 CHF Submit
Future Internet
futureinternet
- 5.4 2009 15.2 Days 1600 CHF Submit
Information
information
- 4.2 2010 21.8 Days 1600 CHF Submit
Network
network
- - 2021 24.8 Days 1000 CHF Submit
Sci
sci
- - 2019 37.2 Days 1200 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (2 papers)

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Communication
Analyzing the Effect of COVID-19 on Education by Processing Users’ Sentiments
Big Data Cogn. Comput. 2023, 7(1), 28; https://doi.org/10.3390/bdcc7010028 - 30 Jan 2023
Viewed by 310
Abstract
COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of [...] Read more.
COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall. Full article
(This article belongs to the Topic Social Computing and Social Network Analysis)
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Article
JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
Appl. Sci. 2022, 12(24), 12709; https://doi.org/10.3390/app122412709 - 11 Dec 2022
Viewed by 307
Abstract
User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few [...] Read more.
User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few in-depth studies into the existing challenges for the joint integration of different types of text attributes, the imbalance between user attributes and network structure, and the utilization of massive unidentified users. To this end, this paper presents a high-accuracy embedding model named Joint embedding of Attributes and Relations for User Alignment (JARUA), to tackle the UA problem. First, a mechanism that can automatically identify the granularity of user attributes is introduced for handling multi-type user attributes. Second, a graph attention network is employed to extract the structural features and is integrated with user attributes features. Finally, an iterative training algorithm with quality filters is introduced to bootstrap the model performances. We evaluate JARUA on two real-world data sets. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art approaches. Full article
(This article belongs to the Topic Social Computing and Social Network Analysis)
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