1st Edition

Subjective Well-Being and Social Media



  • Available for pre-order. Item will ship after August 16, 2021
ISBN 9781138393929
August 16, 2021 Forthcoming by Chapman and Hall/CRC
218 Pages

USD $99.95

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Book Description

Subjective Well-Being and Social Media shows how, by exploiting the unprecedented amount of information provided by the social networking sites, it is possible to build new composite indicators of subjective well-being. These new social media indicators are complementary to official statistics and surveys, whose data are collected at very low temporary and geographical resolution.

The book also explains in full details how to solve the problem of selection bias coming from social media data. Mixing textual analysis, machine learning and time series analysis, the book also shows how to extract both the structural and the temporary components of subjective well-being.

Cross-country analysis confirms that well-being is a complex phenomenon that is governed by macroeconomic and health factors, ageing, temporary shocks and cultural and psychological aspects. As an example, the last part of the book focuses on the impact of the prolonged stress due to the COVID-19 pandemic on subjective well-being in both Japan and Italy. Through a data science approach, the results show that a consistent and persistent drop occurred throughout 2020 in the overall level of well-being in both countries.

The methodology presented in this book:

  • enables social scientists and policy makers to know what people think about the quality of their own life, minimizing the bias induced by the interaction between the researcher and the observed individuals;

  • being language-free, it allows for comparing the well-being perceived in different linguistic and socio-cultural contexts, disentangling differences due to objective events and life conditions from dissimilarities related to social norms or language specificities;
  • provides a solution to the problem of selection bias in social media data through a systematic approach based on time-space small area estimation models.

The book comes also with replication R scripts and data.

Table of Contents

  1. Subjective and Social Well-being
  2. Introduction

    Subjective Well-Being

    Objective Measures

    Multidimensional Indicators

    Surveys

    Social Networking Sites and Data at Scale

    What You’ll Find (and What You’ll Not) in This Book

    Wellbeing, Well Being or Well-Being?

    Gross Domestic Product

    Well-being as a Multidimensional Notion

    The Capability Approach

    Empirical Limitations of the Capability Approach

    Multidimensional Well-Being Indicators

    HDI: Human Development Index

    BLI: Better Life Index

    HPI: Happy Planet Index

    BES: Benessere Equo Sostenibile (Fair Sustainable Well-Being)

    CIW: Canadian Index of Well-Being

    Other Initiatives for Measuring Well-Being

    GNH: Gross National Happiness

    Pros and Cons of Multidimensional Indicators

    Self-Reported Well-Being

    Gallup Surveys

    Gallup World Poll

    Gallup-Sharecare and Global Well-Being Index

    Well-Being Research Based On Gallup Data

    European Social Survey

    World Values Survey

    European Quality of Life Survey

    How to Collect (and Interpret) Self-Reported Evaluations

    Social Networking Sites and Well-Being

    Sentiment Analysis

    Evaluating Subjective Well-Being on the Web

    Pros and cons of large-scale data from SNS

    International and Intercultural Comparisons

    Subjective or Social Well-Being?

    Glossary

  3. Text and Sentiment Analysis
  4. Text Analysis

    Main Principles of Text Analysis

    Different Types of Estimation and Targets

    From Texts to Numbers: How Computers Cruch Documents

    Modelling the Data Coming for Social Networks

    Review of Unsupervised Methods

    Scoring Methods: Wordfish, Wordscores and LLS

    Continuous Space Word Representation: WordVec

    Cluster Analysis

    Topic Models

    Review of Machine Learning Methods

    Decision Trees and Random Forests

    Support Vector Machines

    Artificial Neural Networks

    Estimation of Aggregated Distribution

    The Need of Aggregated Estimation: Reversing the Point of View

    The ReadMe Solution to the Inverse Problem

    The iSA Algorithm

    Main Advantages of iSA over the ReadMe Approach

    The iSAX Algorithm for Sequential Sampling

    Empirical Comparison of Machine Learning Methods

    Confidence Intervals

    Conclusions

    Glossary

  5. Extracting Subjective Well-Being from Textual Data
  6. From SNS Data to Subjective Well-Being Indexes

    Pros & Cons of Twitter Data

    The Hedonometer

    The Gross National Happiness Index

    The World Well-Being Project

    The Twitter Subjective Well-Being Index

    Qualitative Analysis of Texts

    Data Filtering for Training-set Construction

    General Coding Rules

    Specific Coding Rules

    How to Construct the Index

    The Data Collection

    Some Cultural Elements of SNS Communication in Japan

    Preliminary Analysis of the SWB-I & SWB-J Indexes

    Cross-Country Analysis - with Structural Equation Modelling

    Interpretation of the Structural Equation Model

    Glossary

  7. How to Control for Bias in Social Media
  8. Representativeness and Selection Bias of Social Media

    Small Area Estimation Method

    Weighting Strategy

    The Space-Time SAE Model with Weights

    An Application to the Study of Well-Being at Work

    Data and Variables

    The Construction of the Weights

    Official Statistics to Anchor the Model

    Results of the SAE Model

    A Weighted Measure of Well-Being at Work

    The Estimated Measure of Well-Being at Work From the SAE Model

    Comparison with Official Statistics

    Conclusions

    Glossary

  9. Subjective Well-Being and the COVID- Pandemic

         The Year and Well-Being

         The Effect of Lockdown on Gross National Happiness Index

         Hedonometer and the COVID- Pandemic

         The World Well-Being Project and Tracking of Symptoms During the Pandemic

         The Decline of SWB-I & SWB-J During COVID-Related Studies

         Data Collection of Potential Determinants of the SBW Indexes

         COVID- Spread Data

         Financial Data

         Air Quality Data

         Google Search Data

         Google Mobility Data

         Facebook Survey Data

         Restriction Measures Data

         What Impacted The Subjective Well-Being Indexes?

         Preliminary Correlation Analysis

         Monthly Regression Analysis

         Dynamic Elastic Net Analysis

        Analysis of the Italian Data

        Analysis of the Japanese Data

        Comparative Analysis of the Dynamic Elastic Net Results

        Structural Equation Modeling

        Evidence from the Structural Equation Modeling

        Summary of the Results

        Conclusions

        Glossary

 

 

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Author(s)

Biography

Stefano M. Iacus is full professor of Statistics at the University of Milan, on leave at the Joint Research Centre of the European Commission. Former R-core member (1999-2017) and R Foundation Member.

Giuseppe Porro is full professor of Economic Policy at the University of Insubria.

An earlier version of this project was awarded the Italian Institute of Statistics-Google prize for "official statistics and big data".