Big Data Analytics in the Social and Ubiquitous Context

2016 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Social

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 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.

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 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 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.

Big Data

2021 ◽  
pp. 26-29
Author(s):  
Yannick Dufresne ◽  
Brittany I. Davidson
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Big Data ◽  
Online Communities ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
The Social ◽  
Data Volume ◽  
The Web

This chapter assesses big data. Within the social sciences, big data could refer to an emerging field of research that brings together academics from a variety of disciplines using and developing tools to widen perspective, to utilize latent data sets, as well as for the generation of new data. Another way to define big data in the social sciences refers to data corresponding to at least one of the three s of big data: volume, variety, or velocity.. These characteristics are widely used by researchers attempting to define and distinguish new types of data from conventional ones. However, there are a number of ethical and consent issues with big data analytics. For example, many studies across the social sciences utilize big data from the web, from social media, online communities, and the darknet, where there is a question as to whether users provided consent to the reuse of their posts, profiles, or other data shared when they signed up, knowing their profiles and information would be public. This has led to a number of issues regarding algorithms making decisions that cannot be explained. The chapter then considers the opportunities and pitfalls that come along with big data.

Big data analytics: The case of the social security administration

2014 ◽  
Vol 19 (3,4) ◽  
pp. 165-178 ◽  
Author(s):  
Rashmi Krishnamurthy ◽  
Kevin C. Desouza
Keyword(s):  
Big Data ◽  
Social Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Social Security Administration ◽  
The Social
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