Social media, ageism, and older adults during the COVID-19 pandemic
Notice bibliographique
Résumé
The coronavirus disease 2019 (COVID-19) has caused an ongoing pandemic, with over 40 million cases worldwide [[1]CSSE – Center For Systems Science and Engineering at JHU. COVID-19 Map - Johns Hopkins coronavirus resource center. https://coronavirus.jhu.edu/map.html. Accessed 27 October 2020.Google Scholar]. COVID-19 has placed a disproportionate load on disadvantaged populations, including racial minorities, low-income communities, and older individuals. Although older adults carry a significant proportion of the COVID-19 burden, they have been mostly left out of the pandemic response, and few policies have included a focus on aging [[2]United Nations. Policy brief: the impact of COVID-19 on older persons. May 2020. https://www.un.org/development/desa/ageing/wp-content/uploads/sites/24/2020/05/COVID-Older-persons.pdf. Accessed 25 October 2020.Google Scholar]. This is partly due to ageism, or the discrimination of older adults based on age, which has been prevalent in research, policy, and the media. Traditional media (such as newspapers, television, and radio) have long reinforced ageist stereotypes by often portraying older adults as a burden to society. During the pandemic, this phenomenon has also been observed in social media (online platforms for sharing user-generated content) [[2]United Nations. Policy brief: the impact of COVID-19 on older persons. May 2020. https://www.un.org/development/desa/ageing/wp-content/uploads/sites/24/2020/05/COVID-Older-persons.pdf. Accessed 25 October 2020.Google Scholar]. COVID-19 has accelerated changes in the way we share information, with a shift towards social media use by governments, news outlets, and researchers. While this has speeded up the transmission of data, it has also led to the dissemination of misinformation and negative messages. Negative social media messages about COVID-19 and aging often characterize older adults as helpless and expendable individuals. Existing hate speech and intergenerational resentment can be exemplified by the “#BoomerRemover” Twitter hashtag (user-generated metadata term), which was widely shared in social media at the beginning of the pandemic. To understand the dissemination of ageist messages in social media, our group conducted a qualitative analysis of the content English-language tweets about COVID-19 and older adults posted in the ten days following the pandemic declaration [[3]Jimenez-Sotomayor M.R. Gomez-Moreno C. Soto-Perez-de-Celis E Coronavirus, Ageism, and Twitter: an evaluation of tweets about older adults and COVID-19.J Am Geriatr Soc. 2020; 68: 1661-1665Crossref PubMed Scopus (121) Google Scholar]. Our findings showed that almost a quarter of tweets downplayed the importance of COVID-19 because it was deadlier among older individuals, and that 14% contained offensive content or jokes [[3]Jimenez-Sotomayor M.R. Gomez-Moreno C. Soto-Perez-de-Celis E Coronavirus, Ageism, and Twitter: an evaluation of tweets about older adults and COVID-19.J Am Geriatr Soc. 2020; 68: 1661-1665Crossref PubMed Scopus (121) Google Scholar]. Using our same methodology, another group of researchers analyzed 82,893 tweets published between January and April 2020 [[4]Xiang X. Lu X. Halavanau A. et al.Modern senicide in the face of a pandemic: an examination of public discourse and sentiment about older adults and COVID-19 using machine learning.J Gerontol B Psychol Sci Soc Sci. 2020; https://doi.org/10.1093/geronb/gbaa128Crossref PubMed Scopus (61) Google Scholar]. Like in our study, most tweets contained personal opinions, but with a lower proportion of tweets with ageist content of only 12%. Sentiment analysis found that negative tweets mostly contained words related with death and/or sickness, and that sentiments changed over time, with an increase in negative content right after the pandemic declaration and a decrease thereafter [[4]Xiang X. Lu X. Halavanau A. et al.Modern senicide in the face of a pandemic: an examination of public discourse and sentiment about older adults and COVID-19 using machine learning.J Gerontol B Psychol Sci Soc Sci. 2020; https://doi.org/10.1093/geronb/gbaa128Crossref PubMed Scopus (61) Google Scholar]. Ageist social media content seems to be dependent on geography and culture. An unpublished analysis of tweets in Spanish by our group showed that most accounts tweeting about COVID-19 belonged to organizations, and that <10% of tweets had ageist content. Similar results were found when posts on the Chinese social media platform Weibo were analyzed, with most containing positive messages highlighting the contributions of older adults to society [[5]Xi W. Xu W. Zhang X. Ayalon L A thematic analysis of Weibo topics (Chinese Twitter Hashtags) regarding older adults during the COVID-19 outbreak.J Gerontol B Psychol Sci Soc Sci. 2020; https://doi.org/10.1093/geronb/gbaa148Crossref PubMed Scopus (17) Google Scholar]. Differences in ageist content in social media mirror the experiences of older individuals across societies. In the United States, for example, >80% of older adults have felt discriminated because of their age, while this figure is of 18% in Mexico, and only 4% in Spain [6Palmore E.B. Ageism in Canada and the United States.J Cross Cult Gerontol. 2004; 19: 41-46Crossref PubMed Google Scholar, 7Ministerio De Sanidad, Servicios Sociales E Igualdad. Estudio diagnóstico de fuentes secundarias sobre la discriminación en España. https://sid.usal.es/idocs/F8/FDO27095/estudio_discrim_espana.pdf. Accessed 26 October 2020.Google Scholar, 8INEGI. Encuesta nacional sobre discriminación (ENADIS) 2017. https://www.inegi.org.mx/programas/enadis/2017/. Accesed 25 October 2020.Google Scholar]. These variations may be related to the proportion of older adults in a country's population, but also with local social and political issues leading to intergenerational tension and resentment. Ageism in social media can partly be explained by the digital divide between younger and older individuals, with most older adults facing limited access to digital technology. Social media is significantly more popular among individuals aged 15–29 years, and only 7% of Twitter users in the United States are aged ≥65 years [[9]Pew Research Center. Social media fact sheet, https://www.pewinternet.org/fact-sheet/social-media/. 2019. Accessed 7 April 2020.Google Scholar]. Furthermore, older adults generate a limited amount of online content, with a recent study showing that older social media users, and particularly those who are frail or have disabilities, are less likely to post about COVID-19 (OR 0·73) [[10]Campos-Castillo C. Laestadius L.I. Racial and ethnic digital divides in posting COVID-19 content on social media among US adults: secondary survey analysis.J Med Internet Res. 2020; 22: e20472https://doi.org/10.2196/20472Crossref PubMed Scopus (37) Google Scholar]. So how can we fix this (Fig. 1)? There is a need to promote inclusivity in social media and to make the voices of older adults heard. Increasing the participation of older individuals can boost their engagement in social interactions and represent a way to provide and receive social support during the pandemic. Organizations, as well as the media, need to transmit information which is trustworthy and relevant for older adults, and avoid stigmatizing labels and terms [[2]United Nations. Policy brief: the impact of COVID-19 on older persons. May 2020. https://www.un.org/development/desa/ageing/wp-content/uploads/sites/24/2020/05/COVID-Older-persons.pdf. Accessed 25 October 2020.Google Scholar]. Finally, healthcare providers and researchers should expand their social media presence and engage in fact-checking and debunking of myths and hoaxes. Ageism is common in both traditional and social media, and this has increased due to COVID-19, impacting the public's perception of social and economic policies associated with aging. Combating ageism requires a concerted effort form all stakeholders to transmit positive messages associated with aging, and to create an environment of respect, empathy, and solidarity towards older adults during the COVID-19 pandemic. The author has no financial conflicts of interest to disclose.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».