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Big Data Analytics for Social Services
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Big Data Analytics for Social Services

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 19283

Special Issue Editors

Prof. Dr. Sunil Choenni

E-Mail Website1 Website2
Guest Editor
Professor at Rotterdam University of Applied Sciences (RUAS), Research Centre Creating 010, Rotterdam, The Netherlands
Head of Statistical Data and Policy Analysis Division (SIBa), Research and Documentation Centre (WODC), Ministry of Justice and Security, The Hague, The Netherlands
Interests: big and open data; privacy; e-government; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mortaza S. Bargh

E-Mail Website1 Website2
Guest Editor
Professor at Rotterdam University of Applied Sciences (RUAS), Research Centre Creating 010, Rotterdam, The Netherlands
Scientific Researcher at Statistical Data and Policy Analysis Division (SIBa), Research and Documentation Centre (WODC), Ministry of Justice and Security, The Hague, The Netherlands
Interests: big and open data; data mining; machine learning; privacy and security by design; privacy and security engineering; risk management

Special Issue Information

Dear Colleagues,

Public organizations, as well as private enterprises, collect data directly as the input necessary for provisioning their services (like contact information of individuals and citizens) or indirectly as the byproduct of their service provisioning (like the process information related to the chain of actions and interventions). Further, data are currently being generated, collected, analyzed, and distributed at a fast-growing pace. This growth is due to the proliferation of many connected devices (such as cameras, smart phones, sensors, and smart household appliances), widespread and intensive usage of social networks, and digital transformation of business and organizational processes and services. All these have presently resulted in the Big Data paradigm.

There is a growing demand to make use of Big Data and develop (new) applications and services that ease our daily lives, create added value for businesses, provide insight into societal phenomena, and guide policymaking processes. Often, Big Data usage does not fully coincide with the purpose for which the data were originally collected. Using the Big Data gathered from various sources and for diverse purposes, for example, can violate fundamental human rights such as privacy, liberty, autonomy, and dignity. Linking various Big Data sources can reveal (new) privacy-sensitive information about individuals, analyzing Big Data can lead to wrong classification of individuals, and even labelling individuals correctly can be harmful and illegal when, for example, individuals become subject to unjustified or unjust discrimination.

The aim of this Special Issue is to foster research on methodologies, concepts, policies, procedures, and technologies that contribute to using Big Data for provisioning meaningful social services in a responsible way (via, e.g., preserving privacy and fairness). We invite researchers and practitioners from academia, industries, and public organizations to present their innovative (applied) research results or novel approaches and methods related, but not limited, to the following topics:

  • Applications areas of Big Data in a responsible way for policymaking and social service provisioning in, for example, public health, healthcare, e-learning, economics, insurance, and business domains;
  • Concepts, procedures, policies, and technologies related to safeguarding human values and rights when using Big Data. Example topics include privacy-enhancing architectures, frameworks, mechanisms and tools, design aspects of sociotechnological systems, and transparency and accountability aspects;
  • Other relevant topics include pilots, use cases, data quality issues, misinterpretation and misunderstanding aspects, and ethical issues.

Note that the submitted work should be related to the general topic of the special issue in some way. In case of any doubt please feel free to contact the guest editors.

Prof. Dr. Sunil Choenni
Prof. Dr. Mortaza S. Bargh
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 submissions that pass pre-check are 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. Big Data and Cognitive Computing 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 1800 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

  • accountability
  • big data
  • data analytics
  • data mining
  • fairness
  • explainability
  • machine learning
  • privacy (and security) by design
  • platforms
  • risk management
  • tools
  • transparency

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Published Papers (3 papers)

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Research

14 pages, 2394 KiB  
Article
RoBERTaEns: Deep Bidirectional Encoder Ensemble Model for Fact Verification
by Muchammad Naseer, Jauzak Hussaini Windiatmaja, Muhamad Asvial and Riri Fitri Sari
Big Data Cogn. Comput. 2022, 6(2), 33; https://doi.org/10.3390/bdcc6020033 - 22 Mar 2022
Cited by 3 | Viewed by 6425
Abstract
The application of the bidirectional encoder model to detect fake news has been widely applied because of its ability to provide factual verification with good results. Good fact verification requires the most optimal model and has the best evaluation to make news readers [...] Read more.
The application of the bidirectional encoder model to detect fake news has been widely applied because of its ability to provide factual verification with good results. Good fact verification requires the most optimal model and has the best evaluation to make news readers trust the reliable and accurate verification results. In this study, we evaluated the application of a homogeneous ensemble (HE) on RoBERTa to improve the accuracy of a model. We improve the HE method using a bagging ensemble from three types of RoBERTa models. Then, each prediction is combined to build a new model called RoBERTaEns. The FEVER dataset is used to train and test our model. The experimental results showed that the proposed method, RoBERTaEns, obtained a higher accuracy value with an F1-Score of 84.2% compared to the other RoBERTa models. In addition, RoBERTaEns has a smaller margin of error compared to the other models. Thus, it proves that the application of the HE functions increases the accuracy of a model and produces better values in handling various types of fact input in each fold. Full article
(This article belongs to the Special Issue Big Data Analytics for Social Services)
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10 pages, 762 KiB  
Article
Estimating Causal Effects When the Treatment Affects All Subjects Simultaneously: An Application
by Chiara Binelli
Big Data Cogn. Comput. 2021, 5(2), 22; https://doi.org/10.3390/bdcc5020022 - 6 May 2021
Cited by 3 | Viewed by 5025
Abstract
Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions [...] Read more.
Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions on global warming. In this paper, we address this question using a machine learning method, which allows estimating causal impacts in settings when a randomized experiment is not feasible. We discuss the conditions under which this method can identify a causal impact, and we find that carbon dioxide emissions are responsible for an increase in average global temperature of about 0.3 degrees Celsius between 1961 and 2011. We offer two main contributions. First, we provide one additional application of Machine Learning to answer causal questions of policy relevance. Second, by applying a methodology that relies on few directly testable assumptions and is easy to replicate, we provide robust evidence of the man-made nature of global warming, which could reduce incentives to turn to biased sources of information that fuels climate change skepticism. Full article
(This article belongs to the Special Issue Big Data Analytics for Social Services)
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14 pages, 1997 KiB  
Article
Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network
by Giuseppe Ciaburro
Big Data Cogn. Comput. 2020, 4(3), 20; https://doi.org/10.3390/bdcc4030020 - 17 Aug 2020
Cited by 35 | Viewed by 6721
Abstract
Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours [...] Read more.
Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area. Full article
(This article belongs to the Special Issue Big Data Analytics for Social Services)
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