BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

744 related articles for article (PubMed ID: 33617460)

  • 1. Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study.
    Zhang C; Xu S; Li Z; Hu S
    J Med Internet Res; 2021 Mar; 23(3):e26482. PubMed ID: 33617460
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.
    Alhuzali H; Zhang T; Ananiadou S
    J Med Internet Res; 2022 Oct; 24(10):e40323. PubMed ID: 36150046
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study.
    Zhang C; Xu S; Li Z; Liu G; Dai D; Dong C
    J Med Internet Res; 2022 Jan; 24(1):e32394. PubMed ID: 34878410
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study.
    Guntuku SC; Purtle J; Meisel ZF; Merchant RM; Agarwal A
    J Med Internet Res; 2021 Jun; 23(6):e27300. PubMed ID: 33939620
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.
    Kwok SWH; Vadde SK; Wang G
    J Med Internet Res; 2021 May; 23(5):e26953. PubMed ID: 33886492
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.
    Liu Y; Yin Z; Ni C; Yan C; Wan Z; Malin B
    J Med Internet Res; 2023 Feb; 25():e42985. PubMed ID: 36790847
    [TBL] [Abstract][Full Text] [Related]  

  • 11. COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.
    Lyu JC; Han EL; Luli GK
    J Med Internet Res; 2021 Jun; 23(6):e24435. PubMed ID: 34115608
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.
    Adikari A; Nawaratne R; De Silva D; Ranasinghe S; Alahakoon O; Alahakoon D
    J Med Internet Res; 2021 Apr; 23(4):e27341. PubMed ID: 33819167
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis.
    Jang H; Rempel E; Roe I; Adu P; Carenini G; Janjua NZ
    J Med Internet Res; 2022 Mar; 24(3):e35016. PubMed ID: 35275835
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Mining of Opinions on COVID-19 Large-Scale Social Restrictions in Indonesia: Public Sentiment and Emotion Analysis on Online Media.
    Tri Sakti AM; Mohamad E; Azlan AA
    J Med Internet Res; 2021 Aug; 23(8):e28249. PubMed ID: 34280116
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Seeking and Providing Social Support on Twitter for Trauma and Distress During the COVID-19 Pandemic: Content and Sentiment Analysis.
    Esener Y; McCall T; Lakdawala A; Kim H
    J Med Internet Res; 2023 Aug; 25():e46343. PubMed ID: 37651178
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.
    Glowacki EM; Wilcox GB; Glowacki JB
    Subst Abus; 2021; 42(1):39-46. PubMed ID: 32970973
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis.
    Monselise M; Chang CH; Ferreira G; Yang R; Yang CC
    J Med Internet Res; 2021 Oct; 23(10):e30765. PubMed ID: 34581682
    [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]
    of 38.