Measuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with Surveys

We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predictors of cyberchondria during the COVID-19 pandemic: A supervised machine learning approach

Background and aims: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. This behaviour has been considered an emerging public health issue, which may have been exacerbated by the COVID-19 pandemic. The present study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time. Method: Self-reported data on cyberchondria severity (before and during the pandemic), attachment style, impulsivity traits, somatic symptoms, COVID-19 related fears, health anxiety, and intolerance of uncertainty were collected from 725 participants using an online survey distributed in French-speaking European countries during the first wave of the COVID-19 pandemic. Results: COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased, whereas the reassurance facet of cyberchondria decreased. Using supervised machine learning regression analyses, the specific COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. Conclusions: These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria.

Well-being and working from home during COVID-19

Purpose The authors track the well-being of individuals across five European countries during the course of the coronavirus disease 2019 (COVID-19) pandemic and relate their well-being to working from home. The authors also consider the role of pandemic-policy stringency in affecting well-being in Europe. Design/methodology/approach The authors have four waves of novel harmonised longitudinal data in France, Italy, Germany, Spain and Sweden, covering the period May-November 2020. Well-being is measured in five dimensions: life satisfaction, a worthwhile life, loneliness, depression and anxiety. A retrospective diary indicates whether the individual was working in each month since February 2020 and if so whether at home or not at home. Policy stringency is matched in per country at the daily level. The authors consider both cross-section and panel regressions and the mediating and moderating effects of control variables, including household variables and income. Findings Well-being among workers is lower for those who work from home, and those who are not working have the lowest well-being of all. The panel results are more mitigated, with switching into working at home yielding a small drop in anxiety. The panel and cross-section difference could reflect adaptation or the selection of certain types of individuals into working at home. Policy stringency is always negatively correlated with well-being. The authors find no mediation effects. The well-being penalty from working at home is larger for the older, the better-educated, those with young children and those with more crowded housing. Originality/value The harmonised cross-country panel data on individuals' experiences during COVID-19 are novel. The authors relate working from home and policy stringency to multiple well-being measures. The authors emphasise the effect of working from home on not only the level of well-being but also its distribution.