Subjective well-being and social media

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- 1. Subjective and Social Well-Being -- 1.1. Introduction -- 1.1.1. Subjective Well-Being -- 1.1.2. Objective Measures -- 1.1.3. Multidimensional Indicators -- 1.1.4. Surveys -- 1.1.5. Social Networking Sites and Data at Scale -- 1.1.6. What You'll Find (and What You'll Not) in This Book -- 1.1.7. Wellbeing, Well Being or Well-Being? -- 1.2. Gross Domestic Product -- 1.3. Well-Being as a Multidimensional Notion -- 1.3.1. The Capability Approach -- 1.3.1.1. Empirical Limitations of the Capability Approach -- 1.3.2. Multidimensional Well-Being Indicators -- 1.3.2.1. HDI: Human Development Index -- 1.3.2.2. BLI: Better Life Index -- 1.3.2.3. HPI: Happy Planet Index -- 1.3.2.4. BES: Benessere Equo Sostenibile (Fair Sustainable Well-Being) -- 1.3.2.5. CIW: Canadian Index of Well-Being -- 1.3.2.6. Other Initiatives for Measuring Well-Being -- 1.3.2.7. GNH: Gross National Happiness -- 1.3.2.8. Pros and Cons of Multidimensional Indicators -- 1.4. Self-Reported Well-Being -- 1.4.1. Gallup Surveys -- 1.4.1.1. Gallup World Poll -- 1.4.1.2. Gallup-Sharecare and Global Well-Being Index -- 1.4.1.3. Well-Being Research Based on Gallup Data -- 1.4.2. European Social Survey -- 1.4.3. World Values Survey -- 1.4.4. European Quality of Life Survey -- 1.4.5. How to Collect (and Interpret) Self-Reported Evaluations -- 1.5. Social Networking Sites and Well-Being -- 1.5.1. Sentiment Analysis -- 1.5.2. Evaluating Subjective Well-Being on the Web -- 1.5.3. Pros and Cons of Large-Scale Data from SNS -- 1.5.4. International and Intercultural Comparisons -- 1.6. Subjective or Social Well-Being? -- 1.7. Glossary -- 2. Text and Sentiment Analysis -- 2.1. Text Analysis -- 2.1.1. Main Principles of Text Analysis -- 2.2. Different Types of Estimation and Targets.

Subjective and Social Well-beingIntroductionSubjective Well-BeingObjective MeasuresMultidimensional IndicatorsSurveysSocial Networking Sites and Data at ScaleWhat You'll Find (and What You'll Not) in This BookWellbeing, Well Being or Well-Being?Gross Domestic ProductWell-being as a Multidimensional NotionThe Capability ApproachEmpirical Limitations of the Capability ApproachMultidimensional Well-Being IndicatorsHDI: Human Development IndexBLI: Better Life IndexHPI: Happy Planet IndexBES: Benessere Equo Sostenibile (Fair Sustainable Well-Being)CIW: Canadian Index of Well-BeingOther Initiatives for Measuring Well-BeingGNH: Gross National HappinessPros and Cons of Multidimensional IndicatorsSelf-Reported Well-BeingGallup SurveysGallup World PollGallup-Sharecare and Global Well-Being IndexWell-Being Research Based On Gallup DataEuropean Social SurveyWorld Values SurveyEuropean Quality of Life SurveyHow to Collect (and Interpret) Self-Reported EvaluationsSocial Networking Sites and Well-BeingSentiment AnalysisEvaluating Subjective Well-Being on the WebPros and cons of large-scale data from SNSInternational and Intercultural ComparisonsSubjective or Social Well-Being?GlossaryText and Sentiment Analysis Text AnalysisMain Principles of Text AnalysisDifferent Types of Estimation and TargetsFrom Texts to Numbers: How Computers Cruch DocumentsModelling the Data Coming for Social NetworksReview of Unsupervised MethodsScoring Methods: Wordfish, Wordscores and LLSContinuous Space Word Representation: WordVecCluster AnalysisTopic ModelsReview of Machine Learning MethodsDecision Trees and Random ForestsSupport Vector MachinesArtificial Neural NetworksEstimation of Aggregated DistributionThe Need of Aggregated Estimation: Reversing the Point of ViewThe ReadMe Solution to the Inverse ProblemThe iSA AlgorithmMain Advantages of iSA over the ReadMe ApproachThe iSAX Algorithm for Sequential SamplingEmpirical Comparison of Machine Learning MethodsConfidence IntervalsConclusionsGlossaryExtracting Subjective Well-Being from Textual DataFrom SNS Data to Subjective Well-Being IndexesPros & Cons of Twitter DataThe HedonometerThe Gross National Happiness IndexThe World Well-Being ProjectThe Twitter Subjective Well-Being IndexQualitative Analysis of TextsData Filtering for Training-set ConstructionGeneral Coding RulesSpecific Coding RulesHow to Construct the IndexThe Data CollectionSome Cultural Elements of SNS Communication in JapanPreliminary Analysis of the SWB-I & SWB-J IndexesCross-Country Analysis - with Structural Equation ModellingInterpretation of the Structural Equation ModelGlossaryHow to Control for Bias in Social MediaRepresentativeness and Selection Bias of Social MediaSmall Area Estimation MethodWeighting StrategyThe Space-Time SAE Model with WeightsAn Application to the Study of Well-Being at WorkData and VariablesThe Construction of the WeightsOfficial Statistics to Anchor the ModelResults of the SAE ModelA Weighted Measure of Well-Being at WorkThe Estimated Measure of Well-Being at Work From the SAE ModelComparison with Official StatisticsConclusionsGlossarySubjective Well-Being and the COVID- PandemicThe Year and Well-BeingThe Effect of Lockdown on Gross National Happiness IndexHedonometer and the COVID- PandemicThe World Well-Being Project and Tracking of Symptoms During the Pandemic The Decline of SWB-I & SWB-J During COVID-Related StudiesData Collection of Potential Determinants of the SBW IndexesCOVID- Spread DataFinancial DataAir Quality DataGoogle Search DataGoogle Mobility DataFacebook Survey DataRestriction Measures DataWhat Impacted The Subjective Well-Being Indexes?Preliminary Correlation AnalysisMonthly Regression AnalysisDynamic Elastic Net AnalysisAnalysis of the Italian DataAnalysis of the Japanese DataComparative Analysis of the Dynamic Elastic Net ResultsStructural Equation ModelingEvidence from the Structural Equation ModelingSummary of the ResultsConclusionsGlossary

"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""--