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New Trends in Social Computing and Its Applications

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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 (30 September 2021) | Viewed by 61596

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Special Issue Editors

Prof. Dr. Stavros Souravlas

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Guest Editor

Department of Applied Informatics, Faculty of Information Science, University of Macedonia, Thessaloniki, Greece
Interests: parallel processing; cloud computing; big data; social computing; computer simulations; hardware modeling and design
Special Issues, Collections and Topics in MDPI journals

Dr. Stefanos Katsavounis

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Co-Guest Editor

Department of Production and Management Engineering, 57100 Xanthi, Greece
Interests: scheduling; RCMPSP; project management; graph theory and modeling; heuristics

Prof. Dr. Sofia Anastasiadou

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Co-Guest Editor

Department of Early Childhood Education, Faculty of Education, University of Western Macedonia, Koila, 50100 Kozani, Greece
Interests: qualitative and quantitative methods in social sciences; applied statistics; implicative statistical analysis; multivariate statistical analysis; biostatistics; meta-analysis; structural equation models; big data; big data applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social computing is a field that combines computing systems and social behavior. In this regard, social behavior, social environments, and social features are generated via software, supporting hardware tools and technology. Social computing applications include social behavior analysis, social network analysis, semantic web applications, security and privacy issues in social networks, social communities, community detection algorithms, big data and social networks, and even information management in a social networking context. Social computing applications such as blogs, Facebook, LinkedIn, Instagram, etc. have gained tremendous popularity and have seriously affected the overall social behavior and lifestyle, even in fields like politics, which has given rise to extensive research in this area. Most of the efforts have been focused on data modeling and mining in order to reveal hidden patterns of social behavior, algorithms to detect communities, and statistical patterns to study the grouping, homogeneity, dynamics, and even diversity found within social networks. Moreover, the big data explosion can reinforce the tendency toward new trend applications and technologies in the field. Apparently, social computing and applications offer a very promising area of research and application, and very interesting theoretical and practical efforts can be made from scientists in different fields, such as computer scientists, mathematicians, statisticians, information management experts, e-business experts, or decision analytics experts.

The main topics of interest include (but are not limited to) the following:

1) Social behavior;

2) Social network analysis;

3) Semantic web;

4) Community detection algorithms;

5) Statistical methods for network analysis;

6) Big data and social networking;

7) Social recommendation;

8) Information management and social networking;

9) Social networking in education;

10) Social networking and marketing/entrepreneurship/innovation/sustainable development/green growth;

11) Social networking and politics/e-governance/global political and region economy;

12) Digital healthcare;

13) Social networking and data analysis method/data analytics/data learning analytics.

Prof. Dr. Stavros Souravlas
Dr. Stefanos Katsavounis
Prof. Dr. Sofia Anastasiadou
Guest Editors

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Keywords

social networking
community detection
social network analysis
social behavior
data analytics
algorithms
semantic web

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Published Papers (11 papers)

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Research

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16 pages, 709 KiB

Open AccessArticle

The Impact of Information Systems Implementation in the Greek Manufacturing Enterprises

by Paraskevi Kapetanopoulou, Antigoni Kouroutzi and Sofia Anastasiadou

Appl. Sci. 2021, 11(24), 11781; https://doi.org/10.3390/app112411781 - 11 Dec 2021

Cited by 5 | Viewed by 2251

Abstract

Purpose—The purpose of this study is to assess the impact of Information Systems (ISs) implementation in Greek industry. The main issues that are explored through this survey in regards to IS adoption are the financial and nonfinancial benefits that are derived due to IS adoption. The study also investigates the effect that IS adoption had in several business areas in regards to the factor of financial performance. Design/methodology/approach—The survey that was conducted was questionnaire based. Of the 96 valid responses that were received, 83 of them implemented at least one IS. Those 83 responses were analyzed statistically. Several statistical tools were used for that, such as: nonparametric

χ^{2}

tests for homogeneity, Cronbach Alpha method for the reliability of the questionnaire, and Mann–Whitney U tests. Findings—The results suggest that the majority of industry in Greece has implemented—at least—the ERP to conduct their business. In addition, most of them use a combination of not more than three ISs. The respondents are also satisfied by the financial impact of IS adoption. Inventory and warehouse management, along with customer service, were most positively affected by IS implementation. On the other hand, returned products reduction and the relationship with the suppliers were less positively affected by IS adoption. Research limitation/implications—The study has a limitation of being conducted in North Greece and not in the whole country. Originality/value—The paper constitutes an empirical research in regarding the financial and nonfinancial contribution of IS adoption in Greek industry. There are rather limited studies that have been conducted in Greece regarding IS implementation and the impact it poses in business affairs. The financial crisis along with the political instability that Greece has faced in the last decade makes it interesting to explore the influence of IS adoption in manufacturing enterprises. Usually, those studies are conducted in more developed countries where the financial and political environment is more stable. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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24 pages, 3475 KiB

Open AccessArticle

Age Estimates from Name Characters

by Jung-Shiuan Liou, Ching-Yen Hsiao, Lork-Yee Chow, Yen-Hao Huang and Yi-Shin Chen

Appl. Sci. 2021, 11(20), 9611; https://doi.org/10.3390/app11209611 - 15 Oct 2021

Cited by 1 | Viewed by 2606

Abstract

Traditionally, we have been attempting to extract useful features from the massive amount of data generated daily. However, following the legal constraints regarding personal data protection and the challenges of potential data biases and manipulation, artificial intelligence that relies less on big data and more on reasoning ability has become an emerging trend. This paper demonstrates how to estimate age and gender using names only. The proposed two-layer comparative model was trained on Taiwanese names, and its generalizability was further examined on bilingual and cross-border names. By considering additional features of the contextual environment, the model achieves high accuracy in age and gender prediction on Taiwanese and bilingual names. However, the prediction results for ethnic-Chinese Malaysian names (in English) do not reach the same level. This is due to the linguistic differences among Chinese dialects; the features trained on Taiwanese names cannot be directly applied to English names in Malaysia. This study illustrates a path for accomplishing prediction tasks using minimal data and highlights a future possibility for further research. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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13 pages, 1031 KiB

Open AccessArticle

Identifying Potential Managerial Personnel Using PageRank and Social Network Analysis: The Case Study of a European IT Company

by Jan Y. K. Chan, Zhihao Wang, Yunbo Xie, Carlos A. Meisel, Jose D. Meisel, Paula Solano and Heidy Murillo

Appl. Sci. 2021, 11(15), 6985; https://doi.org/10.3390/app11156985 - 29 Jul 2021

Cited by 5 | Viewed by 2219

Abstract

Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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18 pages, 5931 KiB

Open AccessArticle

Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

by Dimitrios Amanatidis, Ifigeneia Mylona, Irene (Eirini) Kamenidou, Spyridon Mamalis and Aikaterini Stavrianea

Appl. Sci. 2021, 11(9), 4281; https://doi.org/10.3390/app11094281 - 9 May 2021

Cited by 15 | Viewed by 5300

Abstract

Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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25 pages, 16017 KiB

Open AccessArticle

Bot Datasets on Twitter: Analysis and Challenges

by Luis Daniel Samper-Escalante, Octavio Loyola-González, Raúl Monroy and Miguel Angel Medina-Pérez

Appl. Sci. 2021, 11(9), 4105; https://doi.org/10.3390/app11094105 - 30 Apr 2021

Cited by 20 | Viewed by 7495

Abstract

The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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26 pages, 2779 KiB

Open AccessArticle

Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks

by Asep Maulana and Martin Atzmueller

Appl. Sci. 2021, 11(9), 4005; https://doi.org/10.3390/app11094005 - 28 Apr 2021

Cited by 5 | Viewed by 2424

Abstract

Anomaly detection in complex networks is an important and challenging task in many application domains. Examples include analysis and sensemaking in human interactions, e.g., in (social) interaction networks, as well as the analysis of the behavior of complex technical and cyber-physical systems such as suspicious transactions/behavior in financial or routing networks; here, behavior and/or interactions typically also occur on different levels and layers. In this paper, we focus on detecting anomalies in such complex networks. In particular, we focus on multi-layer complex networks, where we consider the problem of finding sets of anomalous nodes for group anomaly detection. Our presented method is based on centrality-based many-objective optimization on multi-layer networks. Starting from the Pareto Front obtained via many-objective optimization, we rank anomaly candidates using the centrality information on all layers. This ranking is formalized via a scoring function, which estimates relative deviations of the node centralities, considering the density of the network and its respective layers. In a human-centered approach, anomalous sets of nodes can then be identified. A key feature of this approach is its interpretability and explainability, since we can directly assess anomalous nodes in the context of the network topology. We evaluate the proposed method using different datasets, including both synthetic as well as real-world network data. Our results demonstrate the efficacy of the presented approach. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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17 pages, 1387 KiB

Open AccessArticle

An Empirical Study of Drivers, Barriers, and Cost Efficiency of Information Systems in Greek Industry

by Paraskevi Kapetanopoulou and Antigoni Kouroutzi

Appl. Sci. 2021, 11(8), 3475; https://doi.org/10.3390/app11083475 - 13 Apr 2021

Cited by 4 | Viewed by 2125

Abstract

The business environment is characterized by high complexity and competitiveness. This is the reason why information is considered to be the most valuable source companies have available. From the social point of view, information technology (IT) has a positive effect on business performance, creates strong and transparent relations with enterprise’s clients, and increases competition. The purpose of this study is to assess the current state of information systems (ISs) adoption by Greek industry. The main issues that are explored through this survey in regards to IS adoption is the extent, the most important specific drivers and barriers, and the profitability due to IS adoption. The study also investigates whether motivators or inhibitors are affected by the factor of profitability due to IS adoption. The survey that was conducted is questionnaire-based and the responses were analyzed statistically using several statistical tools. The results suggest that the majority of the industries in Greece have adopted IS to conduct their business. Reducing errors that occur during their business activities seems to be their main motivation for IS adoption. On the other hand, they are hesitant to invest in an IS because of its cost. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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20 pages, 550 KiB

Open AccessArticle

Citizens’ Perception of Smart Cities: A Case Study

by Athanasios Georgiadis, Panayiotis Christodoulou and Zinon Zinonos

Appl. Sci. 2021, 11(6), 2517; https://doi.org/10.3390/app11062517 - 11 Mar 2021

Cited by 24 | Viewed by 6705

Abstract

The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80% of the global population is expected to be living in urban areas. To become smart, a city should develop an approach of services that will focus mainly on citizens to be the primary beneficiaries of the services offered by a Smart City. In this work, we present through a survey of 545 participants, the citizens’ perception about the smart city concept and reveal the Greek and Cypriot citizens’ level of knowledge regards to a Smart City’s actions, applications, and elements. The final results of this study revealed several interesting outcomes. Firstly, this study showed that Cypriot citizens seem to know better what a “Smart City” is compared to Greek citizens, secondly, the study revealed that a large number of participants do not believe that any efforts have been made in their city in order to become “smart” and finally, regards to the most important challenges for the development of a smart city, the survey disclose that the cooperation of the private and public sector is the biggest challenge that needs to be tackled so as citizens can move towards a “smarter” future. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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12 pages, 2514 KiB

Open AccessArticle

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

by Solomiia Fedushko, Oleg Mastykash, Yuriy Syerov and Tomas Peracek

Appl. Sci. 2020, 10(24), 9122; https://doi.org/10.3390/app10249122 - 20 Dec 2020

Cited by 16 | Viewed by 2400

Abstract

This article discusses the relevant task of analyzing user data in the process of managing various web projects. The results of this analysis will help to improve the management of diverse web projects during crises. The authors explore the concept of data heterogeneity in web projects, classify web projects by function and purpose, and analyze the search models and data display in web projects. The proposed algorithms for analyzing user data in the process of managing diverse web projects will improve the structuring and presentation of data on the web project platform. The model user data analysis complex developed by the authors will simplify the process of managing various web projects during crises. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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15 pages, 1725 KiB

Open AccessArticle

Factors of the Technology Acceptance Model for Construction IT

by Eun Soo Park and Min Seo Park

Appl. Sci. 2020, 10(22), 8299; https://doi.org/10.3390/app10228299 - 23 Nov 2020

Cited by 31 | Viewed by 20132

Abstract

The use of information technology is spreading in the construction field. However, the use of information technology in the construction field does not conform to the requirements and characteristics of users who use information technology. This fact is blindly accepted by the government and client demands, which is an impediment to the dissemination of information technology in the construction field. To improve the use of information technology in the construction field, this study analyzes the factors of acceptance of information technology according to the characteristics of users who use information technology in the construction field based on Davis’ technology acceptance model. As a result of the analysis, we found that if users consider IT in the construction industry easy to use, spontaneous attitude and behavioral intention are to be expected. Moreover, acceptance type, educational satisfaction, usage enjoyment, and usage experience are the factors that impact perceived usefulness, and educational satisfaction and usage enjoyment impact perceived ease of use as well. This study aims to derive factors that maximize the approachability and usefulness of users through the use of a technology acceptance model in construction prior to the application of new information technology in the construction field. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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Review

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20 pages, 349 KiB

Open AccessReview

A Survey on the Recent Advances of Deep Community Detection

by Stavros Souravlas, Sofia Anastasiadou and Stefanos Katsavounis

Appl. Sci. 2021, 11(16), 7179; https://doi.org/10.3390/app11167179 - 4 Aug 2021

Cited by 29 | Viewed by 3524

Abstract

In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends. Full article

(This article belongs to the Special Issue New Trends in Social Computing and Its Applications)

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