1st Edition

Subjective Well-Being and Social Media

By Stefano M. Iacus, Giuseppe Porro Copyright 2022
    220 Pages
    by Chapman & Hall

    220 Pages
    by Chapman & Hall

    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.

    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".

    1. Subjective and Social Well-being
    2. Introduction

      Subjective Well-Being

      Objective Measures

      Multidimensional Indicators


      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?


    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



    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


    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



    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






    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".

    "Besides considering the problem of well-being estimation per se, the book presents a great compendium of methods helpful for students and specialists working on various projects which need getting big data from the net sources for statistical research in social studies."
    -Stan Lipovetsky in Technometrics, October 2021

    "[...] the authors present a detailed introduction to the concept of subjective well-being, citing the work both of psychologists and economists. An account of the methods used to measure subjective well-being, and in particular those relevant to social network data is given, making this work of interest to a wide range of researchers and advanced students, including economists, psychologists, statisticians and data scientists. An exposition of the technical issues involved in text and sentiment analysis, as well as a thorough account of existing techniques and methodologies, provides the necessary background for those new to this area. ... Closely referenced and clearly written, researcher’s and advanced students in all related fields, will find this a useful, informative and eminently readable book."
    - Dawn Holmes in Journal of the Royal Statistical Society, Series A, June 2022