308 related articles for article (PubMed ID: 34125051)
1. Emergency Medicine Influencers' Twitter Use During the COVID-19 Pandemic: A Mixed-methods Analysis.
Leibowitz MK; Scudder MR; McCabe M; Chan JL; Klein MR; Trueger NS; McCarthy DM
West J Emerg Med; 2021 Mar; 22(3):710-718. PubMed ID: 34125051
[TBL] [Abstract][Full Text] [Related]
2. What's All the Chatter? A Mixed-Methods Analysis of Emergency Physicians' Tweets.
Riddell J; Brown A; Robins L; Nauman R; Yang J; Jauregui J
West J Emerg Med; 2019 Dec; 21(1):26-32. PubMed ID: 31913814
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. The Saudi Ministry of Health's Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study.
Alhassan FM; AlDossary SA
JMIR Public Health Surveill; 2021 Jul; 7(7):e27942. PubMed ID: 34117860
[TBL] [Abstract][Full Text] [Related]
5. Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis.
Slavik CE; Buttle C; Sturrock SL; Darlington JC; Yiannakoulias N
J Med Internet Res; 2021 Mar; 23(3):e24883. PubMed ID: 33651705
[TBL] [Abstract][Full Text] [Related]
6. COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.
Ahmed W; Vidal-Alaball J; Downing J; López Seguí F
J Med Internet Res; 2020 May; 22(5):e19458. PubMed ID: 32352383
[TBL] [Abstract][Full Text] [Related]
7. Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.
Park HW; Park S; Chong M
J Med Internet Res; 2020 May; 22(5):e18897. PubMed ID: 32325426
[TBL] [Abstract][Full Text] [Related]
8. Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis.
Taneja SL; Passi M; Bhattacharya S; Schueler SA; Gurram S; Koh C
J Med Internet Res; 2021 Jun; 23(6):e26956. PubMed ID: 33974550
[TBL] [Abstract][Full Text] [Related]
9. Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study.
Abd-Alrazaq A; Alhuwail D; Househ M; Hamdi M; Shah Z
J Med Internet Res; 2020 Apr; 22(4):e19016. PubMed ID: 32287039
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.
Pobiruchin M; Zowalla R; Wiesner M
J Med Internet Res; 2020 Aug; 22(8):e19629. PubMed ID: 32790641
[TBL] [Abstract][Full Text] [Related]
14. Dissemination of Anesthesia Information During the Coronavirus Disease 2019 Pandemic Through Twitter: An Infodemiology Study.
Gai N; So D; Siddiqui A; Steinberg BE
Anesth Analg; 2021 Aug; 133(2):515-525. PubMed ID: 33886509
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.
Wang J; Zhou Y; Zhang W; Evans R; Zhu C
J Med Internet Res; 2020 Nov; 22(11):e22152. PubMed ID: 33151894
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis.
Drescher LS; Roosen J; Aue K; Dressel K; Schär W; Götz A
JMIR Public Health Surveill; 2021 Dec; 7(12):e31834. PubMed ID: 34710054
[TBL] [Abstract][Full Text] [Related]
19. Characteristics of Japanese physician influencers on Twitter during the COVID-19 pandemic and fact-checking their tweets on COVID-19-related drugs.
Suzuki T; Tanimoto T; Kamamoto S; Ozaki A; Torii HA; Hase D; Murayama A; Yoshimura H; Uno K
Postgrad Med J; 2024 Jan; 100(1180):91-95. PubMed ID: 37968828
[TBL] [Abstract][Full Text] [Related]
20. Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.
Ng JY; Abdelkader W; Lokker C
BMC Complement Med Ther; 2022 Apr; 22(1):105. PubMed ID: 35418205
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
