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Special Issue "Big Data Research for Social Sciences and Social Impact"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (10 April 2019)

Special Issue Editors

Guest Editor
Prof. Anna Visvizi

1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Website | E-Mail
Interests: smart cities; migration; innovation networks; international business; political economy; economic integration; politics; EU, Central Europe, China
Guest Editor
Prof. Miltiadis D. Lytras

1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Website | E-Mail
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management
Guest Editor
Dr. Kwok Tai Chui

Department of Electronic Engineering, City University of Hong Kong, Hong Kong
E-Mail
Interests: data analytics; artificial intelligence; biomedical signal processing; bioinformatics; optimization; expert systems

Special Issue Information

Dear Colleagues,

Social good is generally an action or application that benefits society. In the past, it was usually driven by governments and non-profit organizations. With the advancement of social media via computer-mediated technologies like WeChat, WhatsApp, Weibo, Twitter, Instagram, Facebook, and YouTube, billions of registered users utilize social interactions through social media. As a result, everyone can contribute to society easily and achieve social good.

Tremendous growth of digital information (from granular data to aggregated data) is available for numerous social sciences and social impact applications, for instance, environmental protection, healthcare and education. Data analytics are ubiquitous and purpose-oriented in different forms: Descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Typical challenges for adopting big data technologies for social sciences and social impact are data handling and storage, data quality, computational power of computer, algorithm customization for special application and security. More importantly, it seems that the humans have an infinite need of utilizing the value of big data for social impact and thus applications on social good. We have to prioritize the applications and more research efforts will be devoted to more important issues.

This Special Issue aims to consolidate recent advances in big data for social good. Pilot studies and projects are especially welcome.

Topics of interest for this Special Issue include (but are not limited to):

  • Innovative applications of data analytics to social sciences and social impact problems like energy, healthcare, education, food, poverty, injustice, and inequalities in society
  • Machine learning algorithms for big data applications for social sciences and social impact
  • Advanced techniques for handling unstructured, unlabeled and/or missing data
  • Data quality control of big data for social good
  • Big data research driven policy making for social impact
  • Standardization for big data infrastructure and framework
  • Big data implications to society
  • Big data driven KPIs research for International Benchmarking
  • Social inclusive economic development and growth through big data applications
  • Human centric big data research
  • Ethical issues on social research of big data
  • Sustainability for social impact research
  • Sustainable data-driven ecosystems for international collaboration
  • Multi-objective optimization
  • Meta-analysis
  • Security and privacy
  • Co-simulation

Prof. Anna Visvizi
Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Guest Editors

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 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. Sustainability is an international peer-reviewed open access semimonthly 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 1700 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 research
  • Social and humanistic computing
  • Social sciences
  • Social good
  • Social impact
  • Machine learning
  • Knowledge management
  • Web science
  • Data science
  • Social inclusive economic growth
  • Sustainability
  • Innovation
  • Innovation networks
  • Semantics
  • Cloud computing
  • Silk road

Published Papers (16 papers)

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Research

Open AccessArticle
Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study
Sustainability 2019, 11(10), 2833; https://doi.org/10.3390/su11102833
Received: 7 April 2019 / Revised: 2 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
PDF Full-text (2929 KB)
Abstract
The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree [...] Read more.
The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
Open AccessArticle
Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China
Sustainability 2019, 11(10), 2822; https://doi.org/10.3390/su11102822
Received: 13 February 2019 / Revised: 8 May 2019 / Accepted: 13 May 2019 / Published: 17 May 2019
PDF Full-text (18274 KB) | HTML Full-text | XML Full-text
Abstract
In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space [...] Read more.
In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Skill Needs for Early Career Researchers—A Text Mining Approach
Sustainability 2019, 11(10), 2789; https://doi.org/10.3390/su11102789
Received: 9 April 2019 / Revised: 10 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
PDF Full-text (5194 KB) | HTML Full-text | XML Full-text
Abstract
Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on [...] Read more.
Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Analyzing Online Car Reviews Using Text Mining
Sustainability 2019, 11(6), 1611; https://doi.org/10.3390/su11061611
Received: 27 February 2019 / Revised: 13 March 2019 / Accepted: 13 March 2019 / Published: 17 March 2019
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Abstract
Consumer reviews on the web have rapidly become an important information source through which consumers can share their experiences and opinions about products and services. It is a form of text-based communication that provides new possibilities and opens vast perspectives in terms of [...] Read more.
Consumer reviews on the web have rapidly become an important information source through which consumers can share their experiences and opinions about products and services. It is a form of text-based communication that provides new possibilities and opens vast perspectives in terms of marketing. Reading consumer reviews gives marketers an opportunity to eavesdrop on their own consumers. This paper examines consumer reviews of three different competitive automobile brands and analyzes the advantages and disadvantages of each vehicle using text mining and association rule methods. The data were collected from an online resource for automotive information, Edmunds.com, with a scraping tool “ParseHub” and then processed in R software for statistical computing and graphics. The paper provides detailed insights into the superior and problematic sides of each brand and into consumers’ perceptions of automobiles and highlights differences between satisfied and unsatisfied groups regarding the best and worst features of the brands. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology
Sustainability 2019, 11(5), 1484; https://doi.org/10.3390/su11051484
Received: 13 February 2019 / Revised: 4 March 2019 / Accepted: 5 March 2019 / Published: 11 March 2019
PDF Full-text (3268 KB) | HTML Full-text | XML Full-text
Abstract
Identification of prevalent problems is an important process of strategic innovation for stakeholders of trending technologies. This paper proposes a systematic and replicable method of patent analysis to identify problems to be solved requisite for sustainable technology planning and development, by implementing the [...] Read more.
Identification of prevalent problems is an important process of strategic innovation for stakeholders of trending technologies. This paper proposes a systematic and replicable method of patent analysis to identify problems to be solved requisite for sustainable technology planning and development, by implementing the concept of ‘context’ to facilitate problem identification. The main concept of the method entails the importance of the connections between contextual information and problems to provide more focused, relevant, and constructive insights essential for instating goals for research and development activities. These context–problem entities and their entwined connections are discovered using keyword pattern matching, grammar-based text mining, and co-word analysis techniques. The intermediary outputs are then utilized to generate the proposed context–problem network (CP net) for social network, grammar, and quantitative data analysis. For verification, our method was applied to 737 patents in the wireless energy transmission technology domain, successfully yielding CP net data. The detailed analysis of the resulting CP net data delivered meaningful information in the wireless charging technology field: The main contexts, “batteries”, “power transmission coils”, and “cores”, are found to be most relevant to the main problems, “maximizing coupling efficiency”, “minimizing DC signal components”, and “charging batteries”. The results provide a wide range of informative perspectives for individuals, the scientific community, corporate, and market-level stakeholders. Furthermore, the method of this study can be applicable to various technologies since it is independent of specific subject domains. Future research directions aim to improve this method for better quality and modeling of contexts and problems. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions
Sustainability 2019, 11(3), 913; https://doi.org/10.3390/su11030913
Received: 20 January 2019 / Revised: 2 February 2019 / Accepted: 7 February 2019 / Published: 11 February 2019
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Abstract
Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of [...] Read more.
Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Towards Sustainable Urban Communities: A Composite Spatial Accessibility Assessment for Residential Suitability Based on Network Big Data
Sustainability 2018, 10(12), 4767; https://doi.org/10.3390/su10124767
Received: 22 October 2018 / Revised: 8 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
Cited by 1 | PDF Full-text (3831 KB) | HTML Full-text | XML Full-text
Abstract
Suitable allocation of residential public services is vital to realizing sustainable communities and cities. By combining network big data and spatial analysis, we developed a composite spatial accessibility assessment method for residential suitability of urban public services covering healthcare, leisure, commerce, transportation, and [...] Read more.
Suitable allocation of residential public services is vital to realizing sustainable communities and cities. By combining network big data and spatial analysis, we developed a composite spatial accessibility assessment method for residential suitability of urban public services covering healthcare, leisure, commerce, transportation, and education services. Xiamen City, China is the test site. We found that although most facilities were concentrated on Xiamen Island, there were shortages in the per capita transportation and education service supplements compared with the average performance of Xiamen City because of the high local population. Meanwhile, Tong’an had advantages in the amount of public facilities due to its long history of regional development. However, high-quality facilities were deficient there as well as in other off-island districts. The residential communities surrounding transportation, commerce, and healthcare facilities had a similar allocation pattern in Xiamen City, whereas the residential accessibility of education and leisure services showed regional differences. Due to unbalanced regional development, evident inequality could be witnessed by comparing the composite assessment results of residential suitability between the communities on Xiamen Island and those in the off-island Areas. Our study hopes to provide dedicated support for designing sustainable communities and cities, especially for those in developing countries. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Assessing Technology Platforms for Sustainability with Web Data Mining Techniques
Sustainability 2018, 10(12), 4497; https://doi.org/10.3390/su10124497
Received: 12 November 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
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Abstract
Public policies have encouraged the proliferation of technology platforms that support the transition towards sustainable agriculture and the development of innovations in the food system. Provided the difficulty associated with assessing the outputs and outcomes of technology platforms, this work proposes a practical [...] Read more.
Public policies have encouraged the proliferation of technology platforms that support the transition towards sustainable agriculture and the development of innovations in the food system. Provided the difficulty associated with assessing the outputs and outcomes of technology platforms, this work proposes a practical assessment method based on the retrieval and analysis of online documents related to the technology platforms. Concretely, the method consists of applying web scraping techniques to retrieve documents related to a technology platform from the Internet and then applying web data-mining techniques to automatically classify these documents into the functions that the platform should fulfill, which are described from the viewpoint of co-evolution of innovation. Data are automatically processed to obtain a variety of metrics, which are applied to measure the impact of European Technology Platforms (ETPs) on promoting an organic food paradigm. This method provides time-series data that helps to follow the evolution of the different functions of the platform and to describe its lifecycle. It has been applied to one platform taken as a case study, TP Organics, which represents a key platform for stakeholders that promote organic farming and agroecology as core components of an ambitious program for sustainable agriculture. The obtained online-based measures have been proven to assess the global evolution of the platform, its dissemination through the European Union (EU) Member States, and the evolution of the different functions expected to be fulfilled by it regarding the diffusion and promotion of innovations in organic agriculture. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis
Sustainability 2018, 10(11), 4055; https://doi.org/10.3390/su10114055
Received: 6 October 2018 / Revised: 1 November 2018 / Accepted: 1 November 2018 / Published: 5 November 2018
Cited by 1 | PDF Full-text (1547 KB) | HTML Full-text | XML Full-text
Abstract
This paper aims at proposing a quantitative methodology to identify promising research frontiers (RFs) based on bibliographic information of scientific papers and patents. To achieve this, core technological documents are identified by suggesting several indices which measure paper impact, research impact, patent novelty, [...] Read more.
This paper aims at proposing a quantitative methodology to identify promising research frontiers (RFs) based on bibliographic information of scientific papers and patents. To achieve this, core technological documents are identified by suggesting several indices which measure paper impact, research impact, patent novelty, impact, marketability, and the right range to evaluate technological documents and which measure the research capability of research organizations (ROs) such as a RO’s activity, productivity, market competitiveness, and publication impact. The RFs can be identified by clustering core technological documents, and promising indices of each RF which are from the perspectives of growth, impact, marketability, and science-based effect, are calculated to promising RFs. As an illustration, this paper selects the case of pattern recognition technology among various technologies in the information and communication technology sector. To validate the proposed method, emerging technologies on the hype cycle are utilized, allowing analysts to compare the results. Comparing the results derived from scientific papers and patents, the results from scientific papers are proper to suggest themes for research (R) in relatively long-term perspective, whereas the results from patents are appropriate for providing themes for development (D) in terms of relatively short-term view. This approach can assist research organizations and companies in devising a technology strategy for a future direction of research and development. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Semantic Network Analysis of Legacy News Media Perception in South Korea: The Case of PyeongChang 2018
Sustainability 2018, 10(11), 4027; https://doi.org/10.3390/su10114027
Received: 5 October 2018 / Revised: 24 October 2018 / Accepted: 26 October 2018 / Published: 2 November 2018
Cited by 1 | PDF Full-text (6310 KB) | HTML Full-text | XML Full-text
Abstract
This paper aims at exploring how conservative and liberal newspapers in South Korea framed PyeongChang 2018 directly. Our research questions addressed four points: first, different attitudes of conservative and liberal newspapers in the PyeongChang news reporting; second, their success and failure in influencing [...] Read more.
This paper aims at exploring how conservative and liberal newspapers in South Korea framed PyeongChang 2018 directly. Our research questions addressed four points: first, different attitudes of conservative and liberal newspapers in the PyeongChang news reporting; second, their success and failure in influencing public opinion; third, South Koreans’ perceptions on PyeongChang 2018; and fourth, South Korean public reliance on the newspapers. To investigate the framing differences, we employed a big data analytic method (automated semantic network analysis) with NodeXL (analytic software). Conclusively, we were able to find out four main findings. First, the conservative media showed pessimistic attitudes to the Olympics, and the liberal media did conversely. Second, despite the conservative media’s resourcefulness, they could not succeed in influencing public opinion. Third, the conservative media perceived the Olympics as an undesirable event, but the liberal media did the Olympics as a significant event for further peace promotion. Fourth, the conservative media’s framings did not considerably influence upon the public opinion. As a conclusion, the public are no longer passive recipients of the messages from the media. Instead, they tend to selectively accept the information from the media based on ‘collective intelligence’. This trend provides a significant implication for enhancing the sustainability of the media environment in South Korea. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data
Sustainability 2018, 10(10), 3734; https://doi.org/10.3390/su10103734
Received: 11 September 2018 / Revised: 8 October 2018 / Accepted: 15 October 2018 / Published: 17 October 2018
Cited by 1 | PDF Full-text (2543 KB) | HTML Full-text | XML Full-text
Abstract
The main aim of this paper is to provide a theoretically and empirically grounded discussion on big data and to propose a conceptual framework for big data based on a temporal dimension. This study adopts two research methods. The first research method is [...] Read more.
The main aim of this paper is to provide a theoretically and empirically grounded discussion on big data and to propose a conceptual framework for big data based on a temporal dimension. This study adopts two research methods. The first research method is a critical assessment of the literature that aims to identify the concept of big data in organizations. This method is composed of a search for source materials, the selection of the source materials, and their analysis and synthesis. It has been used to develop a conceptual framework for assessing an organization’s readiness to adopt big data. The purpose of the second research method is to provide an initial verification of the developed framework. This verification consisted of conducting qualitative research with the use of an in-depth interview in 15 selected organizations. The main contribution of this study is the Temporal Big Data Maturity Model (TBDMM) framework, which can help to measure the current state of an organization’s big data assets, and to plan their future development with respect to sustainability issues. The proposed framework has been built over a time dimension as a fundamental internal structure with the goal of providing a complete means for assessing an organization’s readiness to process the temporal data and knowledge that can be found in modern information sources. The proposed framework distinguishes five maturity levels: atemporal, pre-temporal, partly temporal, predominantly temporal, and temporal, which are used to evaluate data/knowledge, information technology (IT) solutions, functionalities offered by IT solutions, and the sustainable development context. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
The Missing Variable in Big Data for Social Sciences: The Decision-Maker
Sustainability 2018, 10(10), 3415; https://doi.org/10.3390/su10103415
Received: 23 August 2018 / Revised: 18 September 2018 / Accepted: 19 September 2018 / Published: 25 September 2018
Cited by 1 | PDF Full-text (3436 KB) | HTML Full-text | XML Full-text
Abstract
The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” [...] Read more.
The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
(Smart) Citizens from Data Providers to Decision-Makers? The Case Study of Barcelona
Sustainability 2018, 10(9), 3252; https://doi.org/10.3390/su10093252
Received: 30 July 2018 / Revised: 7 September 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
Cited by 2 | PDF Full-text (578 KB) | HTML Full-text | XML Full-text
Abstract
Against the backdrop of the General Data Protection Regulation (GDPR) taking effect in the European Union (EU), a debate emerged about the role of citizens and their relationship with data. European city authorities claim that (smart) citizens are as important to a successful [...] Read more.
Against the backdrop of the General Data Protection Regulation (GDPR) taking effect in the European Union (EU), a debate emerged about the role of citizens and their relationship with data. European city authorities claim that (smart) citizens are as important to a successful smart city program as data and technology are, and that those citizens must be convinced of the benefits and security of such initiatives. This paper examines how the city of Barcelona is marking a transition from the conventional, hegemonic smart city approach to a new paradigm—the experimental city. Through (i) a literature review, (ii) carrying out twenty in-depth interviews with key stakeholders, and (iii) actively participating in three symposiums in Barcelona from September 2017 to March 2018, this paper elucidates how (smart) citizens are increasingly considered decision-makers rather than data providers. This paper considers (i) the implications of the technopolitics of data ownership and, as a result, (ii) the ongoing implementation of the Digital Plan 2017–2020, its three experimental strategies, and the related seven strategic initiatives. This paper concludes that, from the policy perspective, smartness may not be appealing in Barcelona, although the experimental approach has yet to be entirely established as a paradigm. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews
Sustainability 2018, 10(9), 3156; https://doi.org/10.3390/su10093156
Received: 14 July 2018 / Revised: 30 August 2018 / Accepted: 31 August 2018 / Published: 4 September 2018
Cited by 2 | PDF Full-text (1797 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the review voting in online communities, which allows users to express their own opinions in terms of User-generated Content (UGC). However, the sustainable development of online communities is likely to be affected by the social influence of UGC. In [...] Read more.
This paper focuses on the review voting in online communities, which allows users to express their own opinions in terms of User-generated Content (UGC). However, the sustainable development of online communities is likely to be affected by the social influence of UGC. In this paper, we study the so-called crowd intelligence paradox of review voting in online communities. The crowd intelligence paradox means that the quality of reviews is not highly connected with the increasing of review votes. This implies that a review with many votes is likely to be of low quality, and a review with few votes is likely to be of high quality. The crowd intelligence paradox existing in online communities inhibits users’ wishes of participating in social networks and may impact the sustainable development of online communities. Aiming to demonstrate the existence of the crowd intelligence paradox in online communities, we first analyzed a large set of reviews crawled from Net Ease Cloud Music, which is one of the most popular online communities in China. The maximum likelihood (ML) and the hierarchical regression approaches are used in this step. Then, we construct a new research model called the Voting Adoption Model (VAM) to study how different factors impact the crowd intelligence paradox in online communities. Particularly, we propose six hypotheses based on the VAM model and conduct experiments based on the measurement model and the structural model to evaluate the hypotheses. The results show that the quality of reviews is not influential to review votes, and the hot-site attribute is a dominant factor influencing review voting. In addition, the variables of the VAM model, including information credibility, perceived ease of use, and social influence have significant impacts on review voting. Finally, based on the empirical study, we present some research implications and suggestions for online communities to realize healthy and sustainable development in the future. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks
Sustainability 2018, 10(8), 2731; https://doi.org/10.3390/su10082731
Received: 3 July 2018 / Revised: 30 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
Cited by 1 | PDF Full-text (4500 KB) | HTML Full-text | XML Full-text
Abstract
In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This [...] Read more.
In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Open AccessArticle
Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem
Sustainability 2018, 10(8), 2709; https://doi.org/10.3390/su10082709
Received: 8 June 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 1 August 2018
Cited by 2 | PDF Full-text (2250 KB) | HTML Full-text | XML Full-text
Abstract
While applications of big data have been extensively studied, discussion is mostly made from the perspectives of computer science, Internet services, and informatics. Alternatively, this article takes the big data approach as an institutional innovation and uses the problem of illegal subdivided units [...] Read more.
While applications of big data have been extensively studied, discussion is mostly made from the perspectives of computer science, Internet services, and informatics. Alternatively, this article takes the big data approach as an institutional innovation and uses the problem of illegal subdivided units (ISUs) in Hong Kong as a case study. High transaction costs incurred in identification of suspected ISUs and associated enforcement actions lead to a proliferation of ISUs in the city. We posit that the deployment of big data analytics can lower these transaction costs, enabling the government to tackle the problem of illegal accommodations. We propose a framework for big data collection, analysis, and feedback. As the findings of a structured questionnaire survey reveal, building professionals believed that the proposed framework could reduce transaction costs of ISU identification. Yet, concerns associated with the big data approach like privacy and predictive policing were also raised by the professionals. Full article
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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