Big Data Analytics in Cognitive Social Media and Literary Texts: Theory and Praxis

Preface
Acknowledgements
Introduction
Contents
About the Editors
1 The Concept of Cognitive Social Media and Cognitive Literary Studies
	1.1 Introduction
	1.2 Cognitive and Its Aspects
	1.3 Literature and the Function of Literary Studies
	1.4 Poetry
	1.5 Prose
	1.6 Fiction or Novel
	1.7 Works of Art into Text
	1.8 Translation as Cognitive Literary Text
	1.9 Translation as Literary Texts
	1.10 Social and Political Factors of Shahid’s and Faiz’s Poetry
	1.11 Translation as a Medium of Reformation
	1.12 Translation as Cultural Human Capital
	1.13 Big Data Analytics in Literary Texts
	1.14 Why Big Data?
	1.15 Criticism of Big Data Analytics
	1.16 Types of Analysis and W5 Formula in BDA
	1.17 BDA in Humanities and Social Sciences
	1.18 Cyberculture
	1.19 Emergence of Digital Humanities and Big Data Analytics
	1.20 Conclusion
	References
2 Big Data Analytics for Market Prediction via Consumer Insight
	2.1 Big Data
		2.1.1 Big Data Types
		2.1.2 Storage of Big Data
		2.1.3 Three Characteristics of Big Data
		2.1.4 Uncertainty Prediction Using Big Data
	2.2 Big Data Analytics
		2.2.1 Big Data Analytics Types
	2.3 Key Performance Indicators
		2.3.1 Types of KPIs
	2.4 Segmentation
		2.4.1 Segmentation as Clustering
		2.4.2 Segmentation as Classification
	2.5 Targeting
		2.5.1 Behavioral Approach
		2.5.2 Demographics
		2.5.3 Transaction Response
		2.5.4 Sentimental Manifestation
		2.5.5 Targeting Via Traditional Approach Versus Big Data Approach
	2.6 Positioning
	2.7 Association Rules
	2.8 Case Studies
		2.8.1 Walmart
		2.8.2 eBay
		2.8.3 Alibaba
		2.8.4 Amazon
		2.8.5 McDonald’s
	2.9 Conclusion
	References
3 Deconstructive Big Data Analytics: Literary Texts Analysis Through Atlas.ti Software
	3.1 Introduction: What is Deconstruction?
	3.2 Deconstruction is Not Only a Method
	3.3 Transdisciplinary Facets of Deconstruction
	3.4 Deconstruction in Literary Theory
	3.5 Meta-Discourse: Levonorgestrel Implications of Philosophical Discourses
	3.6 Why Are the Literary Texts Complex?
	3.7 Fluidities of Literary Texts and Semanalysis
	3.8 Understanding Deconstruction for Big Data Analytics
	3.9 Reading Philosophy as Literature: Case Study-1
	3.10 Reading Literature as Philosophy: Case Study-2
	3.11 Reading Literature Increases Analytic Skills
	3.12 Deconstructive Big Data Analytics
	3.13 Conclusion
	References
4 Study of Big Data Analytics Tool: Apache Spark
	4.1 Notion of Machine Learning with Big Data
		4.1.1 Sources of Big Data
		4.1.2 Big Data Characteristics
		4.1.3 Applications of Big Data
	4.2 Apache Spark
		4.2.1 Architecture of Apache Spark Components and API
		4.2.2 Difference Between Hadoop and Apache Spark
		4.2.3 Basic Programming in Apache Spark
		4.2.4 Basic Functions in Apache Spark
		4.2.5 Calculating Sum Using Apache Spark
		4.2.6 Calculating Mean Using Apache Spark
		4.2.7 Calculating Standard Deviation Using Apache Spark
	4.3 Data Frame in Apache Spark
		4.3.1 Data Frame Operations Using Apache Spark
		4.3.2 Python Spark SQL (Pyspark)
	4.4 Unsupervised Learning with Apache Spark
		4.4.1 Introduction to Clustering
		4.4.2 Introduction to K-Means Clustering
		4.4.3 Objective of K-Means
		4.4.4 Using K-means in Apache SparkML
	4.5 Supervised Learning with Apache Spark
		4.5.1 Linear Regression
		4.5.2 Steps to Create a Regression
		4.5.3 Programming Demonstration Using R Language
		4.5.4 Logistic Regression
		4.5.5 Logistic Regression with Apache SparkML
	4.6 Conclusion
	References
5 Contemporary Social Media and IoT-Based Pandemic Control: Exploring Possibilities of Big Data Analytics for Healthcare Governance
	5.1 Introduction: Social Media for Contemporary Healthcare
	5.2 The Pandemic: Coronavirus
	5.3 Social Media and the Current Situation
	5.4 Big Data Analytics
	5.5 Big Data Analytics Observation Through Social Media for Covid-19
	5.6 Tools Used for Big Data Analytics
	5.7 Big Data to Cure Covid-19
		5.7.1 Coronavirus Diagnosis/Treatment
		5.7.2 Challenges that Comes During This Pandemic
	5.8 Discussion
		5.8.1 Process Involved in IoT for Healthcare
		5.8.2 Interconnected Hospital
		5.8.3 Internet of HealthCare
		5.8.4 Covidapp
		5.8.5 Registration
		5.8.6 Map Positioning
		5.8.7 Start Consultation
		5.8.8 Diagnosis
		5.8.9 Confirmed Diagnosis
		5.8.10 Suspected Diagnosis
		5.8.11 Treatment
		5.8.12 Mild
		5.8.13 Moderated
		5.8.14 Severe
		5.8.15 Challenges
	5.9 Conclusion
	References
6 Analyzing Women Health-Related Quality of Life Using Sentiment Analysis on Social Media
	6.1 Introduction
	6.2 Literature Study
	6.3 Sentiment Analysis
		6.3.1 Emotion-Based Sentiment Word Extraction
		6.3.2 Sentiment Analysis Using Classifiers
	6.4 Experiment Results
		6.4.1 Training Data Collection
		6.4.2 Emotion-Based Classifiers
	6.5 Conclusion
	References
7 Necessity of Big Data Analytics in Social Media for Questioning the Existence and Survival of Women and the Marginalized People
	7.1 Introduction
	7.2 Aesthetics of Existence
	7.3 Women and the Marginalized During Pandemic
	7.4 Gender-Biased Violence and IPV During Pandemic
	7.5 The Marginalized Dislocation
	7.6 Why Big Data Analytics for Social and Psychological Impact of the Pandemic
	7.7 Women’s Condition
	7.8 Meaning of the “Marginalized”: Notions from Philosophy to Public
	7.9 Deconstructing the Marginality and Minority Writings
	7.10 Women’s Suffering Challenged Through Digital NGO
	7.11 Conclusion
	References
8 Big Data Analytics and Cybersecurity: Emerging Trends
	8.1 Introduction
	8.2 Fundamentals of Big Data
	8.3 Cybersecurity in Cloud Computing
	8.4 Big Data Technologies in Cybersecurity Analytics
		8.4.1 Supervised Method
		8.4.2 Unsupervised Method
	8.5 Emerging Trends in Cybersecurity Analytics in 2019/2020
	8.6 Conclusion
	References
9 Seizing the Networked Crime: Legal Framework for the Governance of Social Media Crimes in India
	9.1 Introduction
	9.2 Objectives
	9.3 Methods
	9.4 Sources of Data and Design
	9.5 Review of Literature
	9.6 Results and Discussion
	9.7 Provisions of Information Technology Act, 2000 Related to Social Media
	9.8 Provisions Related to Social Media in the Indian Penal Code
	9.9 Draft Personal Data Protection Bill, 2019
		9.9.1 Jammu and Kashmir Case
		9.9.2 Sushant Singh Rajput Death Probe Case
		9.9.3 Prashant Bhushan Case
		9.9.4 Ban of Certain Social Media Applications Case
	9.10 Future Prospective
	9.11 Conclusion
	References
10 Toxic Masculinity and Inherent Misogyny on Social Media: Preventive Laws and Indian Judicial Approach
	10.1 Introduction
	10.2 Toxic Masculinity and Inherent Misogyny
	10.3 Toxic Masculinity and Social Media
	10.4 Preventive Legislation
		10.4.1 Resolution L. 13 of UNHRC
		10.4.2 Information Technology Act (2000)
		10.4.3 Indian Penal Code (1860)
	10.5 Judicial Activism in Toxic Masculinity
	10.6 Conclusion
	References
11 Quantifying Human Sentiments Using Qualitative Approach with Semantic-Sentiment Analysis
	11.1 Introduction
		11.1.1 Increasing Importance of Human Sentiments for Business and Academic Research
	11.2 The Building Concepts
		11.2.1 Human Sentiments
		11.2.2 Semantic Analysis
	11.3 Quantifying Human Sentiments: Ways Implied from Research
		11.3.1 Does Qualitative Research Methods Give an Edge?
		11.3.2 Our Way
	11.4 Illustration of Semantic-Sentiment Analysis
		11.4.1 Problem Statement
		11.4.2 Solution
		11.4.3 Inference and Implication
	11.5 Caselets for Practice
		11.5.1 A Caselet of Academic Research
		11.5.2 A Caselet of Business Research
	11.6 Further Discussion
	11.7 Conclusion
	Appendix A.1: Data Sources
	Appendix B.1: RStudio Code Block
	Appendix C.1: Hints for Solving the Caselets
	References
12 Cognitive Transformation Through Social Media
	12.1 Introduction
	12.2 Background
	12.3 Statistics
	12.4 Interacting Virtually and Communicating Globally
	12.5 Interacting Virtually and Communicating Individually
	12.6 Social Media and Social Structure
	12.7 Popular Media and Social Relationships
	12.8 Social Relations and Their Importance
	12.9 Communication Media and Social Institutions
	12.10 Connection Between Media and Public
	12.11 Media and Society Model
	12.12 Social Media and Social Dynamics Through Individual Morals
	12.13 Conclusion
	References
13 Understanding Digital Diaspora as Cognitive Social Media: Necessity of Big Data Analytics for Peace and Harmony
	13.1 Introduction: Diasporas and Digital Diasporas
	13.2 Diaspora is a Social Formation
	13.3 Digital Diasporas and Its Discontents
	13.4 Logic of e-Diaspora, Digital Diaspora, and Big Data
	13.5 Understanding Diasporic Experience in Literary Texts
	13.6 Major Themes of Diaspora Writing
	13.7 Digital Diaspora a World Phenomenon
		13.7.1 Afghanistan Online
		13.7.2 Somalinet
	13.8 Features of Digital Diaspora
	13.9 Cognitive Social Capital: The Benefits of Cognitive Social Media
	13.10 Conclusion
	References
14 Data Analytics of Psychological Distress and Coping Among Fresh Migrant from North Eastern Region to Bengaluru City
	14.1 Introduction
	14.2 Material and Method
	14.3 Result and Findings
		14.3.1 Socio-Demographic Profile of Migrants
		14.3.2 Push and Pull Factors for Migration
		14.3.3 Challenges Faced by Migrants
		14.3.4 FMNER Expectation from People and Government
		14.3.5 Psychological Distress
		14.3.6 Coping Style
	14.4 Discussion
	14.5 Limitations and Implications
	14.6 Conclusion
	References
15 Security and Privacy Challenges for Big Data on Social Media
	15.1 Introduction
	15.2 Overview
		15.2.1 Facebook’s Privacy Model—Exposure, Wreck, Invasion, and Social Convergence
		15.2.2 Facebook Privacy Settings: Who Cares?
		15.2.3 Location Privacy Protection with Personalized k-Anonymity: Algorithms and Architecture
	15.3 Problem Statement
	15.4 System Analysis
		15.4.1 Existing System
	15.5 Proposed System
	15.6 Threat Analysis
		15.6.1 Damaging Media Awareness in Big Datasets
		15.6.2 Service Privacy Analysis
		15.6.3 Social Media Meta Data Survey
	15.7 Conclusion
	References
16 Social Media: The Dark Horse of Market in Consumer Decision Journey
	16.1 Social Media as a Source of Social Media Big Data
		16.1.1 Social Media Models and Behavioral Predictions
	16.2 Behavioral Predictions: Marketer’s Guide Through the Consumer Decision Journey
	16.3 Co-creating Value Through Social Media Big Data
	16.4 Optimum Utilization of Social Media Big Data for Insightful Behavioral Predictions
		16.4.1 Identification of the Users
		16.4.2 Multi-scale Analysis for Big Data and Social Media
		16.4.3 Eliminating the Noise from Big Data and Social Media
		16.4.4 Big Data Content Linked with Big Data Context
		16.4.5 Multidisciplinary Collaborations for Big Data
	16.5 Conclusion
	References