330 related articles for article (PubMed ID: 32785620)
1. Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning.
Xiang X; Lu X; Halavanau A; Xue J; Sun Y; Lai PHL; Wu Z
J Gerontol B Psychol Sci Soc Sci; 2021 Mar; 76(4):e190-e200. PubMed ID: 32785620
[TBL] [Abstract][Full Text] [Related]
2. Coronavirus, Ageism, and Twitter: An Evaluation of Tweets about Older Adults and COVID-19.
Jimenez-Sotomayor MR; Gomez-Moreno C; Soto-Perez-de-Celis E
J Am Geriatr Soc; 2020 Aug; 68(8):1661-1665. PubMed ID: 32338787
[TBL] [Abstract][Full Text] [Related]
3. Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.
Xue J; Chen J; Hu R; Chen C; Zheng C; Su Y; Zhu T
J Med Internet Res; 2020 Nov; 22(11):e20550. PubMed ID: 33119535
[TBL] [Abstract][Full Text] [Related]
4. #BoomerRemover: COVID-19, ageism, and the intergenerational twitter response.
Skipper AD; Rose DJ
J Aging Stud; 2021 Jun; 57():100929. PubMed ID: 34082999
[TBL] [Abstract][Full Text] [Related]
5. Ageism in COVID-Related Newspaper Coverage: The First Month of a Pandemic.
Jen S; Jeong M; Kang H; Riquino M
J Gerontol B Psychol Sci Soc Sci; 2021 Oct; 76(9):1904-1912. PubMed ID: 34096609
[TBL] [Abstract][Full Text] [Related]
6. Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study.
Margus C; Brown N; Hertelendy AJ; Safferman MR; Hart A; Ciottone GR
J Med Internet Res; 2021 Jul; 23(7):e28615. PubMed ID: 34081612
[TBL] [Abstract][Full Text] [Related]
7. Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis.
Ainley E; Witwicki C; Tallett A; Graham C
J Med Internet Res; 2021 Oct; 23(10):e31101. PubMed ID: 34469327
[TBL] [Abstract][Full Text] [Related]
8. "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.
Gerts D; Shelley CD; Parikh N; Pitts T; Watson Ross C; Fairchild G; Vaquera Chavez NY; Daughton AR
JMIR Public Health Surveill; 2021 Apr; 7(4):e26527. PubMed ID: 33764882
[TBL] [Abstract][Full Text] [Related]
9. Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.
Hung M; Lauren E; Hon ES; Birmingham WC; Xu J; Su S; Hon SD; Park J; Dang P; Lipsky MS
J Med Internet Res; 2020 Aug; 22(8):e22590. PubMed ID: 32750001
[TBL] [Abstract][Full Text] [Related]
10. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
Boon-Itt S; Skunkan Y
JMIR Public Health Surveill; 2020 Nov; 6(4):e21978. PubMed ID: 33108310
[TBL] [Abstract][Full Text] [Related]
11. Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach.
Alshalan R; Al-Khalifa H; Alsaeed D; Al-Baity H; Alshalan S
J Med Internet Res; 2020 Dec; 22(12):e22609. PubMed ID: 33207310
[TBL] [Abstract][Full Text] [Related]
12. A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study.
Xue J; Shier ML; Chen J; Wang Y; Zheng C; Chen C
J Med Internet Res; 2024 May; 26():e51698. PubMed ID: 38718390
[TBL] [Abstract][Full Text] [Related]
13. Calculated Ageism: Generational Sacrifice as a Response to the COVID-19 Pandemic.
Barrett AE; Michael C; Padavic I
J Gerontol B Psychol Sci Soc Sci; 2021 Mar; 76(4):e201-e205. PubMed ID: 32841334
[TBL] [Abstract][Full Text] [Related]
14. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.
Xue J; Chen J; Chen C; Zheng C; Li S; Zhu T
PLoS One; 2020; 15(9):e0239441. PubMed ID: 32976519
[TBL] [Abstract][Full Text] [Related]
15. Twitter discussions on breastfeeding during the COVID-19 pandemic.
Jagarapu J; Diaz MI; Lehmann CU; Medford RJ
Int Breastfeed J; 2023 Nov; 18(1):56. PubMed ID: 37925408
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.
Mackey T; Purushothaman V; Li J; Shah N; Nali M; Bardier C; Liang B; Cai M; Cuomo R
JMIR Public Health Surveill; 2020 Jun; 6(2):e19509. PubMed ID: 32490846
[TBL] [Abstract][Full Text] [Related]
17. From Hostile to Benevolent Ageism: Polarizing Attitudes Toward Older Adults in German COVID-19-Related Tweets.
Døssing MV; Crăciun IC
Gerontologist; 2022 Sep; 62(8):1185-1195. PubMed ID: 35581153
[TBL] [Abstract][Full Text] [Related]
18. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.
Chandrasekaran R; Mehta V; Valkunde T; Moustakas E
J Med Internet Res; 2020 Oct; 22(10):e22624. PubMed ID: 33006937
[TBL] [Abstract][Full Text] [Related]
19. Using Twitter to Understand COVID-19 Vaccine-Related Ageism During the Pandemic.
Bacsu JR; Andrew MK; Azizi M; Berger C; Cammer A; Chasteen AL; Fraser SA; Grewal KS; Green S; Gowda-Sookochoff R; Mah JC; McGilton KS; Middleton L; Nanson K; Spiteri RJ; Tang Y; O'Connell ME
Gerontologist; 2024 Feb; 64(2):. PubMed ID: 37267449
[TBL] [Abstract][Full Text] [Related]
20. Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis.
Berkovic D; Ackerman IN; Briggs AM; Ayton D
J Med Internet Res; 2020 Dec; 22(12):e24550. PubMed ID: 33170802
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
