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Oxford University Press - PMC COVID-19 Collection logoLink to Oxford University Press - PMC COVID-19 Collection
. 2022 May 17:gnac063. doi: 10.1093/geront/gnac063

From Hostile to Benevolent Ageism: Polarising Attitudes Towards Older Adults in German COVID-19 Related Tweets

Mille Viktoria Døssing 1,, Irina Catrinel Crăciun 2,3
PMCID: PMC9129152  PMID: 35581153

Abstract

Background and Objectives

Previous studies have linked COVID-19 to a rise in ageism. While a growing body of research examined hostile ageism during the pandemic, benevolent ageism received less attention. Drawing on the stereotype content theory and the classic tripartite model of attitudes, the current study explored how benevolent and hostile ageism are reflected in the cognitive, affective, and behavioural dimensions of attitudes towards older adults in German COVID-19 related tweets. The study examined the most prevalent attitudes as well as changes in prevalence between the first and second lockdown period in Germany.

Research Design and Methods

792 German tweets concerning COVID-19 and ageing were collected and coded using Mayring’s qualitative content analysis with a dominantly inductive approach. Quantitative methods were used to identify the most prevalent subthemes as well as changes in prevalence.

Results

The coding resulted in 21 subthemes. Most tweets (60.73%) contained either hostile or benevolent ageist attitudes, with benevolent ageism being more prevalent. The top 5 subthemes in terms of prevalence and reach contained several opposing attitudes, such as devaluation and opposing devaluation. The chi-square tests revealed a shift from a promotion to an evaluation of COVID-19 related policies between the two lockdowns.

Discussion and Implications

Results highlight social media’s polarising effect and its potential contribution to both hostile and benevolent ageism in the context of COVID-19 in Germany. Results indicate the need to consider the adverse effects of benevolent ageism and use of chronological age as risk factor, when designing COVID-19 related policies.

Keywords: Stereotype content model, Twitter, Qualitative content analysis, Age stereotypes, Coronavirus pandemic

Contributor Information

Mille Viktoria Døssing, Freie Universität Berlin, Berlin, Germany.

Irina Catrinel Crăciun, Freie Universität Berlin, Berlin, Germany; Babeș-Bolyai University, Cluj, Romania.

Supplementary Material

gnac063_suppl_Supplementary_Material

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gnac063_suppl_Supplementary_Material

Articles from The Gerontologist are provided here courtesy of Oxford University Press

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