Social Computing and Social Network Analysis

Abstract submission deadline

closed (30 September 2023)

Manuscript submission deadline

closed (31 December 2023)

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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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800
Future Internet futureinternet	3.4	6.7	2009	11.8 Days	CHF 1600
Information information	3.1	5.8	2010	18 Days	CHF 1600
Network network	-	-	2021	18.2 Days	CHF 1000
Sci sci	-	3.1	2019	47.7 Days	CHF 1200

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Published Papers (8 papers)

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All Applied Sciences BDCC Future Internet Information Network Sci

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25 pages, 11632 KiB  

Open AccessArticle

Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis

by Dante Conti, Carlos Eduardo Gomez, Juan Guillermo Jaramillo and Victoria Eugenia Ospina

Appl. Sci. 2023, 13(18), 10338; https://doi.org/10.3390/app131810338 - 15 Sep 2023

Cited by 1 | Viewed by 671

Abstract

The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a [...] Read more.

The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a structured method for data modeling is implemented based on the KDD methodology. Tweets from January to June 2022 related to the companies in the sector are processed, and a temporal analysis of the evolution of sentiment is performed based on the dictionaries Bing, AFINN, and NRC. Subsequently, the LDA algorithm (Latent Dirichlet Allocation algorithm) is used to visually identify the topics with the greatest negative impact reported by the users in each of the 6 months by adding the temporal dimension. The results revealed that, for Aqueduct (water supply service), the topic with the highest dissatisfaction is related to the “Water Tank Request” processes; for Enel (energy services) “Service Outages”; and for Vanti (gas services), “Case solution and request information”. Temporal patterns of tweets, sentiments, and topics are also highlighted for the three companies. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

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18 pages, 4299 KiB  

Open AccessArticle

Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient

by Pengyue Li, Liang Wei, Haiping Ding, Faxu Li and Feng Hu

Appl. Sci. 2023, 13(14), 8212; https://doi.org/10.3390/app13148212 - 14 Jul 2023

Viewed by 749

Abstract

The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old [...] Read more.

The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with adjustable clustering coefficients is proposed. Subsequently, we use the SIS (susceptible–infectious–susceptible) model to describe the information propagation process in the aggregation-phenomenon hypernetwork. In addition, we establish the relationship between the density of informed nodes and the structural parameters of the hypernetwork in a steady state using the mean field theory. Notably, modifications to the clustering coefficients do not impact the hyperdegree distribution; however, an increase in the clustering coefficients results in a reduced speed of information dissemination. It is further observed that the model can degenerate to a BA (Barabási–Albert) hypernetwork by setting the clustering coefficient to zero. Thus, the aggregation-phenomenon hypernetwork is an extension of the BA hypernetwork with stronger applicability. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

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14 pages, 312 KiB  

Open AccessReview

A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception

by Alice Wang, Rozita Dara, Samira Yousefinaghani, Emily Maier and Shayan Sharif

Big Data Cogn. Comput. 2023, 7(2), 72; https://doi.org/10.3390/bdcc7020072 - 12 Apr 2023

Cited by 2 | Viewed by 3337

Abstract

Infectious diseases take a large toll on the global population, not only through risks of illness but also through economic burdens and lifestyle changes. With both emerging and re-emerging infectious diseases increasing in number, mitigating the consequences of these diseases is a growing [...] Read more.

Infectious diseases take a large toll on the global population, not only through risks of illness but also through economic burdens and lifestyle changes. With both emerging and re-emerging infectious diseases increasing in number, mitigating the consequences of these diseases is a growing concern. The following review discusses how social media data, with a focus on textual Twitter data, can be collected and processed to perform disease surveillance and understand the public’s attitude toward policies around the control of emerging infectious diseases. In this paper, we review machine learning tools and approaches that were used to determine the correlation between social media activity in disease trends within regions, understand the public’s opinion, or public health leaders’ approaches to disease presentation. While recent models migrated toward popular deep learning methods, neural networks and algorithms that optimized existing models were also explored as new standards for social media data analysis in disease prediction and monitoring. As adherence to public health policies can be improved by understanding and responding to major concerns identified by sentiment analyses, the advancements and challenges in understanding text sentiment are also discussed. Recent sentiment classifiers include more complex classifications and can even recognize epidemiological considerations that affect the spread of outbreaks. The comprehensive integration of locational and epidemiological considerations with advanced modeling capabilities and sentiment analysis will produce robust models and more precision for both disease monitoring and prediction. Accurate real-time disease outbreak prediction models will provide health organizations with the capability to address public concerns and to initiate outbreak responses proactively rather than reactively. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

19 pages, 1185 KiB  

Open AccessArticle

Consumer’s Attitude towards Display Google Ads

by Mohammad Al Khasawneh, Abdel-Aziz Ahmad Sharabati, Shafig Al-Haddad, Rania Al-Daher, Sarah Hammouri and Sima Shaqman

Future Internet 2023, 15(4), 145; https://doi.org/10.3390/fi15040145 - 07 Apr 2023

Cited by 1 | Viewed by 3132

Abstract

The context of Display Google ads and its components has significant importance to previous studies. However, the full understanding of the variables that influence both Display Google ads avoidance and intention to click has not been thoroughly acknowledged. Thus, this study aims to [...] Read more.

The context of Display Google ads and its components has significant importance to previous studies. However, the full understanding of the variables that influence both Display Google ads avoidance and intention to click has not been thoroughly acknowledged. Thus, this study aims to outline an entire understanding of the different variables that lead Display Google ads to be avoided or clicked on. A detailed review of previous studies has been completed to illustrate a thorough image of Display Google ads. Accordingly, this study developed a theoretical model combining four variables (Display Google ads’ Prior Experience, Originality, Relevance, and Credibility) that lead to affecting Display Google ads’ Avoidance and Intention to Click, with one mediator (Consumer’s Attitude). A quantitative methodology has been employed, in which an online survey has been used to collect data, which were collected from 358 respondents, then coded against AMOS. The data analysis results show that three independent variables positively impact the intention to click; however, credibility has the highest value, then relevance and originality, consequently., while Display Google ads prior experience had no impact on the intention to click. Finally, the research concluded different practical and theoretical implications, and future potential research, and limitations. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

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15 pages, 540 KiB  

Open AccessArticle

A Mixed Malay–English Language COVID-19 Twitter Dataset: A Sentiment Analysis

by Jeffery T. H. Kong, Filbert H. Juwono, Ik Ying Ngu, I. Gde Dharma Nugraha, Yan Maraden and W. K. Wong

Big Data Cogn. Comput. 2023, 7(2), 61; https://doi.org/10.3390/bdcc7020061 - 27 Mar 2023

Cited by 2 | Viewed by 3212

Abstract

Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we [...] Read more.

Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over $67\%$ in Malay language, $27\%$ in English, $2\%$ in Chinese, and $4\%$ in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

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18 pages, 744 KiB  

Open AccessArticle

An Improved Link Prediction Approach for Directed Complex Networks Using Stochastic Block Modeling

by Lekshmi S. Nair, Swaminathan Jayaraman and Sai Pavan Krishna Nagam

Big Data Cogn. Comput. 2023, 7(1), 31; https://doi.org/10.3390/bdcc7010031 - 09 Feb 2023

Cited by 4 | Viewed by 3034

Abstract

Link prediction finds the future or the missing links in a social–biological complex network such as a friendship network, citation network, or protein network. Current methods to link prediction follow the network properties, such as the node’s centrality, the number of edges, or [...] Read more.

Link prediction finds the future or the missing links in a social–biological complex network such as a friendship network, citation network, or protein network. Current methods to link prediction follow the network properties, such as the node’s centrality, the number of edges, or the weights of the edges, among many others. As the properties of the networks vary, the link prediction methods also vary. These methods are inaccurate since they exploit limited information. This work presents a link prediction method based on the stochastic block model. The novelty of our approach is the three-step process to find the most-influential nodes using the m-PageRank metric, forming blocks using the global clustering coefficient and, finally, predicting the most-optimized links using maximum likelihood estimation. Through the experimental analysis of social, ecological, and biological datasets, we proved that the proposed model outperforms the existing state-of-the-art approaches to link prediction. Full article

(This article belongs to the Topic Social Computing and Social Network Analysis)

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12 pages, 3698 KiB  

Open AccessCommunication

Analyzing the Effect of COVID-19 on Education by Processing Users’ Sentiments

by Mohadese Jamalian, Hamed Vahdat-Nejad, Wathiq Mansoor, Abigail Copiaco and Hamideh Hajiabadi

Big Data Cogn. Comput. 2023, 7(1), 28; https://doi.org/10.3390/bdcc7010028 - 30 Jan 2023

Cited by 1 | Viewed by 1980

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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19 pages, 962 KiB  

Open AccessArticle

JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks

by Min Yang, Baiyang Chen and Xiaoliang Chen

Appl. Sci. 2022, 12(24), 12709; https://doi.org/10.3390/app122412709 - 11 Dec 2022

Viewed by 1537

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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