Media attention toward COVID-19 across 18 countries: The influence of cultural values and pandemic severity

Abstract

Background Current media studies of COVID-19 devote asymmetrical attention to social media, in contrast, newspapers have received comparatively less attention. Newspapers are an integral source of current information—that are syndicated and amplified by social media to a wide global audience. This is the first-known study to investigate the impact of cultural values and pandemic severity on media attention towards COVID-19. Findings lay the groundwork for targeted public health communications that are culturally nuanced. Objective We investigated the impact of cultural values and pandemic severity on Media Attention towards COVID-19 across 18 countries. Methods We tracked the global volume of COVID-19 coverage (to measure media attention) over 8 months in a news media database of 12 billion words with 30 million articles from over 7,000 news media sites. Predictors of Media Attention towards COVID-19 came from the Oxford COVID-19 Government Response Tracker (incidence and mortality) and Hofstede’s Cultural Values. Results Media attention toward COVID-19 increased 55 times over 8 months. Higher rates of new cases and deaths predicted this exponential increase. Countries with higher power distance, uncertainty avoidance, and long-term orientation, were associated with increased media attention, controlling for covariates. Conclusions Cultural values play a significant role in the news media’s attention toward COVID-19, controlling for pandemic severity. We provided a framework to design targeted public health communications that are culturally nuanced.

Ageism on Twitter during the COVID‐19 pandemic

“…Next, a sentiment score was calculated using a sentiment library which comprises words rated on a scale from 1 (very negative) to 5 (very positive) by two raters trained in gerontology. This sentiment library has proven valid and reliable in measuring words associated with age stereotypes and is consistent with past corpus‐based analyses (Ng, 2021b; Ng et al., 2015; Ng & Chow, 2021; Ng & Tan, 2021b, 2022). The methodology also supports literature on priming which indicates that the repeated association of negative words with older adults—or words within close lexical proximity—increases implicit ageism (B.…”

Impact of Old Age on an Occupation’s Image Over 210 Years: An Age Premium for Doctors, Lawyers, and Soldiers

“…Insights from this study could be integrated into advocacy efforts to reframe aging (Ng & Indran, 2022c. Notwithstanding the reality of ageism (Ng et al, 2022;Ng & Indran, 2022), our analysis shows that in certain occupational contexts, old age comes with a kind of premium, evoking stereotypes related to experience, knowledge, and skill. These findings make room for the possibility that positive subcategories of older adults may emerge when viewed through their occupational roles.…”

“…If the target noun was the first word of a sentence, collocates from the preceding sentence were excluded; (b) Relevant context: collocate referred specifically to an older person (checked by two raters trained in gerontology); (c) Mutual Information Score of 3 and above, which indicates semantic bonding, meaning the collocate has a stronger association with the respective synonym than other words in the corpus. This is an application of concordance analysis known as “psychomics,” which has been used in previous studies to analyze societal stereotypes (Ng, 2021a, 2021b; Ng et al, 2021c; Ng & Indran, 2021a, 2021b; Ng & Tan, 2021, 2022). In total, there were 675,213 collocates.…”

A playbook for effective age advocacy on Twitter

“…We were hence unable to account for any changes that might have occurred throughout the 12 years, such as the number of followers and level of activity of each organization, both of which will inevitably vary across time. To cultivate greater awareness of ageism, future studies could analyze how to improve age advocacy efforts on other platforms such as news media, 44–47 as well as through techniques such as psychometrics, 48–50 surveys, 48,49,51 and big data 52–57 …”