371 related articles for article (PubMed ID: 35632491)
21. Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis.
Niu Q; Liu J; Kato M; Shinohara Y; Matsumura N; Aoyama T; Nagai-Tanima M
JMIR Infodemiology; 2022; 2(1):e32335. PubMed ID: 35578643
[TBL] [Abstract][Full Text] [Related]
22. Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake.
Yousef M; Dietrich T; Rundle-Thiele S
JMIR Form Res; 2022 Sep; 6(9):e37775. PubMed ID: 36007136
[TBL] [Abstract][Full Text] [Related]
23. Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study.
Liu S; Zhu M; Yu DJ; Rasin A; Young SD
JMIR Ment Health; 2017 Jan; 4(1):e2. PubMed ID: 28073737
[TBL] [Abstract][Full Text] [Related]
24. Public Officials' Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets.
Marani H; Song MY; Jamieson M; Roerig M; Allin S
JMIR Infodemiology; 2023 Jul; 3():e41582. PubMed ID: 37315194
[TBL] [Abstract][Full Text] [Related]
25. Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter.
Massey PM; Leader A; Yom-Tov E; Budenz A; Fisher K; Klassen AC
J Med Internet Res; 2016 Dec; 18(12):e318. PubMed ID: 27919863
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis.
Park S; Suh YK
J Med Internet Res; 2023 Jan; 25():e42623. PubMed ID: 36603153
[TBL] [Abstract][Full Text] [Related]
28. Understanding COVID-19 Halal Vaccination Discourse on Facebook and Twitter Using Aspect-Based Sentiment Analysis and Text Emotion Analysis.
Feizollah A; Anuar NB; Mehdi R; Firdaus A; Sulaiman A
Int J Environ Res Public Health; 2022 May; 19(10):. PubMed ID: 35627806
[TBL] [Abstract][Full Text] [Related]
29. COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.
Karami A; Zhu M; Goldschmidt B; Boyajieff HR; Najafabadi MM
Vaccines (Basel); 2021 Sep; 9(10):. PubMed ID: 34696167
[TBL] [Abstract][Full Text] [Related]
30. Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective.
Garcia-Rudolph A; Laxe S; Saurí J; Bernabeu Guitart M
J Med Internet Res; 2019 Aug; 21(8):e14077. PubMed ID: 31452514
[TBL] [Abstract][Full Text] [Related]
31. Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis.
Mermer G; Özsezer G
Disaster Med Public Health Prep; 2022 Oct; 17():e266. PubMed ID: 36226686
[TBL] [Abstract][Full Text] [Related]
32. Positive attitudes towards COVID-19 vaccines: A cross-country analysis.
Greyling T; Rossouw S
PLoS One; 2022; 17(3):e0264994. PubMed ID: 35271637
[TBL] [Abstract][Full Text] [Related]
33. Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022.
Ng QX; Teo YQJ; Kiew CY; Lim BP; Lim YL; Liew TM
Cyberpsychol Behav Soc Netw; 2023 Aug; 26(8):621-630. PubMed ID: 37358808
[TBL] [Abstract][Full Text] [Related]
34. Vaccine discourse during the onset of the COVID-19 pandemic: Topical structure and source patterns informing efforts to combat vaccine hesitancy.
Hwang J; Su MH; Jiang X; Lian R; Tveleneva A; Shah D
PLoS One; 2022; 17(7):e0271394. PubMed ID: 35895626
[TBL] [Abstract][Full Text] [Related]
35. Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study.
Ferawati K; Liew K; Aramaki E; Wakamiya S
JMIR Infodemiology; 2022; 2(2):e39504. PubMed ID: 36277140
[TBL] [Abstract][Full Text] [Related]
36. Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights.
Stevens H; Rasul ME; Oh YJ
JMIR Infodemiology; 2022; 2(2):e37635. PubMed ID: 36188420
[TBL] [Abstract][Full Text] [Related]
37. Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea.
Shim JG; Ryu KH; Lee SH; Cho EA; Lee YJ; Ahn JH
Int J Environ Res Public Health; 2021 Jun; 18(12):. PubMed ID: 34207016
[TBL] [Abstract][Full Text] [Related]
38. Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers.
Yu H; Yang CC; Yu P; Liu K
PLoS One; 2022; 17(3):e0264794. PubMed ID: 35259181
[TBL] [Abstract][Full Text] [Related]
39. Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study.
Aljedaani W; Abuhaimed I; Rustam F; Mkaouer MW; Ouni A; Jenhani I
Soc Netw Anal Min; 2022; 12(1):128. PubMed ID: 36090696
[TBL] [Abstract][Full Text] [Related]
40. Identifying drivers of COVID-19 vaccine sentiments for effective vaccination policy.
Sufi F; Alsulami M
Heliyon; 2023 Sep; 9(9):e19195. PubMed ID: 37681141
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]