Happiness by Proxy: Real-Time Social Diagnostics from Online Search, Sentiment, population and Risk Behaviour

Abstract

Traditional indicators of population well-being, including GDP and survey-based subjective well being measures, face well-known limitations: GDP excludes the psychological, social, and cultural dimensions of human flourishing, while surveys are temporally lagged, expensive, and vulnerable to response biases. This thesis explores whether naturally occurring digital behavioural data — specifically Google Trends search-volume series — can provide complementary, real-time, population-level proxy indicators of subjective well-being that address some of these limitations. The thesis is situated within the subjective well-being (SWB) tradition, targeting its life-satisfaction and affect components (see 1.4.0). "Proxy" is used in a specific sense (1.4.0(b)): aggregate behavioural indicator, predictive feature, and correlational population-level signal — explicitly not substitute measure for individual well-being. All inferences are at the population or sub-population level. Five empirical studies are presented. Study 1 builds an aggregated index from emotion-related Google Trends keywords for the UK (2020–2023) and shows that it covaries with the ONS weekly life-satisfaction series (R² = 0.6535 in random-split test; chronological-split robustness check reported in 1.3.4). Study 2 compares interpretable machine-learning models for life-satisfaction prediction in a real-time monitoring framework intended as a proof of concept for inclusive digital governance, with portability to other settings flagged as a design intention requiring external validation. Study 3 uses LSTM networks to identify three distinct diurnal patterns in aggregate search behaviour, which we describe as "digital chronotypes" — a property of the population-level signal, not of individual users. Study 4 applies the BEAST change-point algorithm to Arabic language Google Trends data for Saudi Arabia (2013–2022), as an exploratory investigation of how aggregate digital signals respond to cultural transitions including Ramadan and Vision 2030 milestones. Study 5 uses the Culture-Based Development (CBD) framework with a stepwise econometric pipeline (Oaxaca-Blinder, OLS-FE, 3SLS, Heckman, hierarchical) to investigate the association between country-of-origin cultural milieu and observed driving behaviour in a multi national sample of drivers in Saudi Arabia, identifying an empirical pattern (the "Paradox of National Behavioural Patterns") whose substantive and statistical interpretations are discussed. The thesis offers partial empirical support for the use of digital behavioural traces as complementary population-level well-being proxies, indicative evidence that some PERMA dimensions leave detectable traces in aggregate digital behaviour, and proof-of-concept demonstrations of how such proxies might be applied in policy and risk-assessment contexts. It does not establish a unified, universally validated measurement system, and the contributions in each empirical chapter are framed accordingly.