Special Issue "Big Data and Social Sciences"

A special issue of Social Sciences (ISSN 2076-0760).

Deadline for manuscript submissions: closed (15 October 2020).

Special Issue Editors

Prof. Dr. Juan Leiva
E-Mail Website
Guest Editor
Research Group in Inclusive Educational Development and Innovation, Faculty of Education Sciences, University of Malaga, Office 7.06. Boulevard Louis Pasteur, 25, Campus of Teatinos, Department of Didactics and School Organization, University of Malaga, Av. de Cervantes, 2, 29010 Malaga, Spain
Interests: interculturality; inclusive education; teacher training; big data; ICT
Special Issues and Collections in MDPI journals
Dr. Antonio Matas-Terrón
E-Mail Website
Guest Editor
Research Group in Inclusive Educational Development and Innovation, Faculty of Education Sciences, University of Malaga, Office 6.08. Boulevard Louis Pasteur, 25, Campus of Teatinos, C.P. 29010 Malaga, Spain
Interests: big data; educational innovation; measurement; evaluation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

This special edition aims to discuss the potential of Big Data in different areas of social sciences such as education, social work, social psychology, sociology and citizen science.  The speed and rapidity of technological and scientific changes has involved the possibility of compiling massive data whose social analysis can be enormously beneficial for improving the quality of life of human beings. Massive information analysis is an opportunity for responsible, respectful and operational decision-making.

 It Is extremely interesting to address the multiple dimensions and projective functions of the Big Data as the description, diagnosis, prediction and prescription of social and educational proposals for the improvement of public services and devices and the social initiative. In this sense, it is very interesting to analyze the evolution of the training needs of the population, both young people and adults, as well as the elderly, for the establishment of social, cultural, educational and public health proposals that are sustainable in the framework of a society in permanent change, dynamism and complexity.

We anticipate that this special issue will become a cornerstone of scientific literature for the Big Data in social sciences. We are interested in receiving documents from different social disciplines that illustrate through studies, research and reviews the potential for improvement of the Big Data. We also welcome the contributions outlining the positive and critical aspects of the Big Data for a calm analysis of the role it can play in the coming years. In addition, we are interested in contributions that show the analytical link and the learning and development potential of the Big Data for the implementation of improvements in education, social work, health and other services of general interest to society.

We invite you to contribute to this topic by presenting research papers or exhaustive reviews in various fields of social sciences such as education, psychology, Social work, sociology, among others. The documents selected for this special edition are subject to a rigorous peer review procedure with the aim of rapidly and widely disseminating the results of the research, development and applications of the Big Data in different Areas and contexts of scientific and professional development.

Dr. Juan Leiva
Dr. Antonio Matas
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 papers will be 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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences 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 1200 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

  • big data
  • social sciences
  • learning analysis
  • big data in education
  • big data in social work
  • big data training

Published Papers (9 papers)

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Research

Communication
Big Data: Some Ethical Concerns for the Social Sciences
Soc. Sci. 2021, 10(2), 36; https://doi.org/10.3390/socsci10020036 - 24 Jan 2021
Cited by 1 | Viewed by 2191
Abstract
While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the [...] Read more.
While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
Article
Online Discourse in the Context of COVID-19, the First Health Crisis in China after the Advent of Mobile Social Media: A Content Analysis of China’s Weibo and Baidu
Soc. Sci. 2020, 9(10), 167; https://doi.org/10.3390/socsci9100167 - 24 Sep 2020
Cited by 1 | Viewed by 2404
Abstract
The COVID-19 epidemic was the first universal health crisis since China entered the era of mobile social media. When Severe Acute Respiratory Syndrome (SARS) broke out in 2003, it was not until almost six years later that Weibo was born, marking China’s entry [...] Read more.
The COVID-19 epidemic was the first universal health crisis since China entered the era of mobile social media. When Severe Acute Respiratory Syndrome (SARS) broke out in 2003, it was not until almost six years later that Weibo was born, marking China’s entry into the era of mobile social media (Weixin 2020). In this context, this research analysed the role of the social media platform Weibo and the Internet search browser Baidu, in a government controlled online media environment, during the COVID-19 pandemic. In order to undertake this study, we applied the use of content and sentiment analysis to the discourse identified through the topics published during the investigation period, which encompassed 15 December 2019 until 15 March 2020. From the findings of this study, we concluded that, during the pre- and post-COVID-19 period, there was an important presence of social and lifestyle topic categories dominating the online discourse, which dramatically changed in correlation to the increasing spread of the disease. Additionally, there was a marked absence of topics in relation to economic and political information, and there was a notable absence of an official Government “voice” generating topics. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
Tendency to Use Big Data in Education Based on Its Opportunities According to Andalusian Education Students
Soc. Sci. 2020, 9(9), 164; https://doi.org/10.3390/socsci9090164 - 21 Sep 2020
Cited by 1 | Viewed by 1702
Abstract
Big Data is configured as a technological element and of increasing educational interest. The need to advance the quality of academic inclusion has led to an unprecedented expansion of educational processes and features. Thus, collecting massive data on educational information is part of [...] Read more.
Big Data is configured as a technological element and of increasing educational interest. The need to advance the quality of academic inclusion has led to an unprecedented expansion of educational processes and features. Thus, collecting massive data on educational information is part of teachers’ daily lives and educational institutions themselves. There is an intense debate about the potential of Big Data in the educational context, especially through learning analytics that favor the appropriate, responsible, and inclusive use of the data collected. The main aim of this article is to analyze user profiles and the tendency to use Big Data and see what factors influence its applicability. This study employs an incidental sample of 265 students of Educational Sciences from Andalusian Universities, (Spain), using an ad-hoc survey. A cluster analysis was conducted together with ordinal regression analysis and decision tree. The results allow us to confirm the existence of two different student profiles, in terms of their perceptions and appraisal of Big Data and its implications in education. Consequently, a higher score is found for that profile that contemplates and positively conceives Big Data in terms of learning opportunities and improvement of educational quality. The research demonstrates the need to promote Big Data training within the context of university, aiding the acquisition of digital and transversal skills. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy
Soc. Sci. 2020, 9(9), 162; https://doi.org/10.3390/socsci9090162 - 19 Sep 2020
Cited by 3 | Viewed by 2530
Abstract
This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The [...] Read more.
This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
Big Data in Education: Perception of Training Advisors on Its Use in the Educational System
Soc. Sci. 2020, 9(4), 53; https://doi.org/10.3390/socsci9040053 - 15 Apr 2020
Cited by 4 | Viewed by 2194
Abstract
Big Data has revolutionized decision making in many fields, including education. The incorporation of information and communication technologies into education enables us to gather information about the teaching and learning process. As Big Data can help us improve it, it is paramount to [...] Read more.
Big Data has revolutionized decision making in many fields, including education. The incorporation of information and communication technologies into education enables us to gather information about the teaching and learning process. As Big Data can help us improve it, it is paramount to integrate it into initial and continuous learning stages. This study therefore aims at finding out the perception of the training advisors of teacher training centers (N = 117) in Andalusia on the application of Big Data in education. The tool is an adaptation of the VABIDAE (Assessment of Big Data Applied to Education) scale, and the study of the descriptive statistics was carried out by using the analysis of variance (ANOVA) and Mann–Whitney U tests in order to check the existence of significant differences and correlations between the items that make up the scale. The results reflect the positive perception of training advisors on the use of Big Data in education. Significant differences were found in the competence level variable, whereby this tool was better rated by those advisors who feel that they have an advanced competence level. In conclusion, Big Data is valued for its ability to personalize educational processes and the consequent improvement in academic results, which shows the need to increase the level of knowledge about this tool. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
Article
Painting Practical Support: A Study about the Usage of Painting Materials in Children’s Painting Works
Soc. Sci. 2020, 9(4), 33; https://doi.org/10.3390/socsci9040033 - 26 Mar 2020
Cited by 1 | Viewed by 1942
Abstract
Painting materials are one of the mediums that help painters to show the effects of paintings. The use of different painting materials can help the painter to display different painting styles and artistic conception. Six hundred sixty-seven children aged 7 to 13 participated [...] Read more.
Painting materials are one of the mediums that help painters to show the effects of paintings. The use of different painting materials can help the painter to display different painting styles and artistic conception. Six hundred sixty-seven children aged 7 to 13 participated in the study. This study is mainly about the impact of the use of different painting materials on children’s painting creation. The questionnaire survey was conducted based on primary school fine arts education to study the influence of painting materials on children’s painting ability. The content of the questionnaire survey was to investigate children’s usage of different painting materials in painting works and the grasp of painting materials knowledge. This research also provided some painting materials training methods for primary school fine arts teachers to guide children to use different painting materials for painting creation based on the study results. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
Adopt Big-Data Analytics to Explore and Exploit the New Value for Service Innovation
Soc. Sci. 2020, 9(3), 29; https://doi.org/10.3390/socsci9030029 - 18 Mar 2020
Cited by 1 | Viewed by 1947
Abstract
Big-data analytics is gaining substantial attention due to its contribution to the process of determining business strategy and providing valuable information for the design and development of service innovation. The principal objective of this research is to study the adoption of big-data analytics [...] Read more.
Big-data analytics is gaining substantial attention due to its contribution to the process of determining business strategy and providing valuable information for the design and development of service innovation. The principal objective of this research is to study the adoption of big-data analytics for service innovation. The focus will be on leveraging features of data analytics to capture genuine customer’s requirements from the communication data through the digital service channel. This study used mixed methods research of documentary research, with supplementary semi-structured interviews. The interviews were conducted with 11 executive managements who have more than ten years of experience in data analytics or service development. The result of the research found that organizations in the services industry are using big data analytics to build capabilities to gain competitive advantages as well as the ability to rapidly and accurately respond to the market’s demands. The process of adopting big-data analytics for service innovation described in this article consists of seven essential procedural steps that impact the success of the development of service innovation, and also considered with the objective of increasing effectiveness in opportunity identification and reduce complexity in the fuzzy frond-end service innovation development theory. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
Email Based Institutional Network Analysis: Applications and Risks
Soc. Sci. 2019, 8(11), 306; https://doi.org/10.3390/socsci8110306 - 08 Nov 2019
Cited by 3 | Viewed by 2430
Abstract
Social Network Analysis can be applied to describe the patterns of communication within an organisation. We explore how extending standard methods, by accounting for the direction and volume of emails, can reveal information regarding the roles of individual members. We propose an approach [...] Read more.
Social Network Analysis can be applied to describe the patterns of communication within an organisation. We explore how extending standard methods, by accounting for the direction and volume of emails, can reveal information regarding the roles of individual members. We propose an approach that models certain operational aspects of the organization, based on directional and weighted indicators. The approach is transferable to other types of social network with asymmetrical connections among its members. However, its applicability is limited by privacy concerns, the existence of multiple alternative communication channels that evolve over time, the difficulty of establishing clear links between organisational structure and efficiency and, most importantly, the challenge of setting up a system that measures the impact of communication behavior without influencing the communication behaviour itself. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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Article
Big Data in Education. A Bibliometric Review
Soc. Sci. 2019, 8(8), 223; https://doi.org/10.3390/socsci8080223 - 25 Jul 2019
Cited by 13 | Viewed by 2917
Abstract
The handling of a large amount of data to analyze certain behaviors is reaching a great popularity in the decade 2010–2020. This phenomenon has been called Big Data. In the field of education, the analysis of this large amount of data, generated to [...] Read more.
The handling of a large amount of data to analyze certain behaviors is reaching a great popularity in the decade 2010–2020. This phenomenon has been called Big Data. In the field of education, the analysis of this large amount of data, generated to a greater extent by students, has begun to be introduced in order to improve the teaching–learning process. In this paper, it was proposed as an objective to analyze the scientific production on Big Data in education in the databases Web of Science (WOS), Scopus, ERIC, and PsycINFO. A bibliometric study was carried out on a sample of 1491 scientific documents. Among the results, the increase in publications in 2017 and the configuration of certain journals, countries and authors as references in the subject matter stand out. Finally, potential explanations for the study findings and suggestions for future research are discussed. Full article
(This article belongs to the Special Issue Big Data and Social Sciences)
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