148 related articles for article (PubMed ID: 32579119)
1. Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study.
Stevens R; Bonett S; Bannon J; Chittamuru D; Slaff B; Browne SK; Huang S; Bauermeister JA
J Med Internet Res; 2020 Jun; 22(6):e17196. PubMed ID: 32579119
[TBL] [Abstract][Full Text] [Related]
2. Trends and Characteristics of #HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study.
Burgess R; Feliciano JT; Lizbinski L; Ransome Y
JMIR Public Health Surveill; 2022 Aug; 8(8):e35937. PubMed ID: 35969453
[TBL] [Abstract][Full Text] [Related]
3. Toward Using Twitter for PrEP-Related Interventions: An Automated Natural Language Processing Pipeline for Identifying Gay or Bisexual Men in the United States.
Klein AZ; Meanley S; O'Connor K; Bauermeister JA; Gonzalez-Hernandez G
JMIR Public Health Surveill; 2022 Apr; 8(4):e32405. PubMed ID: 35468092
[TBL] [Abstract][Full Text] [Related]
4. Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study.
Liu S; Chen B; Kuo A
J Med Internet Res; 2019 Jun; 21(6):e12394. PubMed ID: 31162126
[TBL] [Abstract][Full Text] [Related]
5. Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study.
Cai M; Shah N; Li J; Chen WH; Cuomo RE; Obradovich N; Mackey TK
PLoS One; 2020; 15(8):e0235150. PubMed ID: 32845882
[TBL] [Abstract][Full Text] [Related]
6. Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter.
Klein AZ; Sarker A; Cai H; Weissenbacher D; Gonzalez-Hernandez G
J Biomed Inform; 2018 Nov; 87():68-78. PubMed ID: 30292855
[TBL] [Abstract][Full Text] [Related]
7. Characterizing Twitter Content About HIV Pre-exposure Prophylaxis (PrEP) for Women: Qualitative Content Analysis.
Keddem S; Agha A; Morawej S; Buck A; Cronholm P; Sonalkar S; Kearney M
J Med Internet Res; 2023 May; 25():e43596. PubMed ID: 37166954
[TBL] [Abstract][Full Text] [Related]
8. Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets.
Sigalo N; St Jean B; Frias-Martinez V
JMIR Public Health Surveill; 2022 Jul; 8(7):e34285. PubMed ID: 35788108
[TBL] [Abstract][Full Text] [Related]
9. Exploring Substance Use Tweets of Youth in the United States: Mixed Methods Study.
Stevens RC; Brawner BM; Kranzler E; Giorgi S; Lazarus E; Abera M; Huang S; Ungar L
JMIR Public Health Surveill; 2020 Mar; 6(1):e16191. PubMed ID: 32213472
[TBL] [Abstract][Full Text] [Related]
10. An Examination of Twitter Data to Identify Risky Sexual Practices Among Youth and Young Adults in Botswana.
Cornelius J; Kennedy A; Wesslen R
Int J Environ Res Public Health; 2019 Feb; 16(4):. PubMed ID: 30813392
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.
Ricard BJ; Hassanpour S
J Med Internet Res; 2021 Sep; 23(9):e27314. PubMed ID: 34524095
[TBL] [Abstract][Full Text] [Related]
13. User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study.
Oh J; Bonett S; Kranzler EC; Saconi B; Stevens R
JMIR Public Health Surveill; 2022 Jun; 8(6):e32718. PubMed ID: 35713945
[TBL] [Abstract][Full Text] [Related]
14. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.
Boligarla S; Laison EKE; Li J; Mahadevan R; Ng A; Lin Y; Thioub MY; Huang B; Ibrahim MH; Nasri B
BMC Med Inform Decis Mak; 2023 Oct; 23(1):217. PubMed ID: 37845666
[TBL] [Abstract][Full Text] [Related]
15. Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis.
Lama Y; Chen T; Dredze M; Jamison A; Quinn SC; Broniatowski DA
J Med Internet Res; 2018 Sep; 20(9):e10244. PubMed ID: 30217792
[TBL] [Abstract][Full Text] [Related]
16. The reliability of tweets as a supplementary method of seasonal influenza surveillance.
Aslam AA; Tsou MH; Spitzberg BH; An L; Gawron JM; Gupta DK; Peddecord KM; Nagel AC; Allen C; Yang JA; Lindsay S
J Med Internet Res; 2014 Nov; 16(11):e250. PubMed ID: 25406040
[TBL] [Abstract][Full Text] [Related]
17. Exploring substance use disorder discussions in Native American communities: a retrospective Twitter infodemiology study.
Calac AJ; McMann T; Cai M; Li J; Cuomo R; Mackey TK
Harm Reduct J; 2022 Dec; 19(1):141. PubMed ID: 36517902
[TBL] [Abstract][Full Text] [Related]
18. Electronic word of mouth on twitter about physical activity in the United States: exploratory infodemiology study.
Zhang N; Campo S; Janz KF; Eckler P; Yang J; Snetselaar LG; Signorini A
J Med Internet Res; 2013 Nov; 15(11):e261. PubMed ID: 24257325
[TBL] [Abstract][Full Text] [Related]
19. Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak.
Sell TK; Hosangadi D; Trotochaud M
BMC Public Health; 2020 May; 20(1):550. PubMed ID: 32375715
[TBL] [Abstract][Full Text] [Related]
20. "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates.
Lamy FR; Daniulaityte R; Zatreh M; Nahhas RW; Sheth A; Martins SS; Boyer EW; Carlson RG
Drug Alcohol Depend; 2018 Feb; 183():248-252. PubMed ID: 29306816
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
