scholarly journals Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171882381 ◽  
Author(s):  
Lucy Resnyansky
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Task Demands ◽  
Digital Data ◽  
Conceptual Frameworks ◽  
Social Scientists ◽  
The Social ◽  
Epistemological Challenge ◽  
Social Scientific

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.

scholarly journals Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Sociology ◽  
2017 ◽  
Vol 51 (6) ◽  
pp. 1132-1148 ◽  
Author(s):  
Susan Halford ◽  
Mike Savage
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Data ◽  
The Social ◽  
The Future

Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

Cloud-Based Big Data Analytics in Smart Educational System

2018 ◽  
pp. 189-199 ◽  
Author(s):  
Newlin Rajkumar Manokaran ◽  
Venkatesa Kumar Varathan ◽  
Shalinie Deepak
Keyword(s):  
Big Data ◽  
Educational System ◽  
Data Analytics ◽  
Low Cost ◽  
Big Data Analytics ◽  
Digital Data ◽  
Individual Student ◽  
Cloud Environment ◽  
Technology In The Classroom ◽  
Digital Era

In this modern Digital era, Technology is a key player in transforming the educational pedagogy for the benefit of students and society at large. Technology in the classroom allows the teacher to deliver more personalized learning to the student with better interaction through the internet. Humongous amount of digital data collected day by day increases has led to the use of big data. It helps to correlate the performance and learning pattern of individual students by analysing large amount of stored activity of the students, offering worthwhile feedback etc. The use of big data analytics in a cloud environment helps in providing an instant infrastructure with low cost, accessibility, usability etc. This paper presents an innovative means towards providing a smarter educational system in schools. It improves individual efficiency by providing a way to monitor the progress of individual student by maintaining a detailed profile. This framework has been established in a cloud environment which is an online learning system where the usage pattern of individual students are collected.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2018 ◽  
pp. 61-78 ◽  
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

Librarian’s perspective for the implementation of big data analytics in libraries on the bases of lean-startup model

2019 ◽  
Vol 36 (1) ◽  
pp. 21-37
Author(s):  
Khurshid Ahmad ◽  
Zheng JianMing ◽  
Muhammad Rafi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Privacy ◽  
Core Competencies ◽  
Big Data Analytics ◽  
Data Availability ◽  
Digital Data ◽  
Data Organization ◽  
Content Type ◽  
Lean Startup

Purpose This study aims to propose a model based on philosophical thoughts of Dr S.R Ranganathan and the lean-startup method for the execution of big data analytics (BDA) in libraries. The research paves a way to understand the role and required competencies of Library and Information Science (LIS) professionals for the implementation of BDA in libraries. Design/methodology/approach In the BDA analytics context, a session with a proposed model was presented to the audience to get the response of librarians about the required competencies and skills. The research tool was developed based on the literature review to know the role of LIS professionals and their required competencies/skills for BDA. The questionnaire was distributed in the BDA session to collect the responses of the participating audience on the variables that focused on the role and core competencies of LIS professionals in BDA. In the analysis of results, the independent t-test was applied to know the mean value of the overall response rate. Findings The findings show that perceptions of LIS professionals in the understanding of BDA ranked high in data privacy, data availability, data organization and data literacy. Digital data curation, policies supervision and providing the data consultancy also showed a significant relationship among these variables. Besides, the correlation between the required skills for BDA, metadata skills, data ethics, data acquisition, data cleaning, data organization, data analysis, digital curation, data clustering, data protection rules and digital visualization also showed a beneficial relationship. Originality/value This study also helps to understand the perspective of LIS professionals for the implementation of BDA in libraries and to fill the literature gap in the respective.

scholarly journals Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kiran Chaudhary ◽  
Mansaf Alam ◽  
Mabrook S. Al-Rakhami ◽  
Abdu Gumaei
Keyword(s):  
Social Media ◽  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Process Data ◽  
Social Media Platform ◽  
The Social ◽  
Social Media Platforms ◽  
Media Platform

AbstractSocial media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

scholarly journals Tackling Delinquency in the Era of Social Credit System: Prospects and Risks

2021 ◽  
Vol 15 (3) ◽  
pp. 282-294
Author(s):  
Roman Rouvinsky ◽  
Evgeny Tsarev
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Control ◽  
Legal System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Legal Liability ◽  
Credit System ◽  
Social Credit ◽  
The Social

The paper is devoted to the changes in fighting delinquency connected to the application of artificial intelligence and Big Data analytics. The focus of the paper has been made on the Social Credit System and related advanced mechanisms of control and surveillance, which are currently being built and implemented in China. The issue of how the latest technologies of social control impact the fight against crimes and administrative offences has been examined. The transforming effect of introduction of the Social Credit System and algorithmic mechanisms of social control upon the legal system and some of its institutions (notably, the legal liability institution, the punishment, the concept of an offender) has been assessed in the paper. The authors come to the conclusion that the introduction of the Social Credit System in China and the development of algorithmic mechanisms of social control and crime prevention may lead to the separation of punishment from the construct of legal liability and the concept of an offence as a guilty deed.

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