149 related articles for article (PubMed ID: 32845882)
1. 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]
2. Characterising communities impacted by the 2015 Indiana HIV outbreak: A big data analysis of social media messages associated with HIV and substance abuse.
Cuomo RE; Cai M; Shah N; Li J; Chen WH; Obradovich N; Mackey TK
Drug Alcohol Rev; 2020 Nov; 39(7):908-913. PubMed ID: 32406155
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access.
Mackey T; Kalyanam J; Klugman J; Kuzmenko E; Gupta R
J Med Internet Res; 2018 Apr; 20(4):e10029. PubMed ID: 29613851
[TBL] [Abstract][Full Text] [Related]
6. Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data.
Katsuki T; Mackey TK; Cuomo R
J Med Internet Res; 2015 Dec; 17(12):e280. PubMed ID: 26677966
[TBL] [Abstract][Full Text] [Related]
7. Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning.
Kalyanam J; Katsuki T; R G Lanckriet G; Mackey TK
Addict Behav; 2017 Feb; 65():289-295. PubMed ID: 27568339
[TBL] [Abstract][Full Text] [Related]
8. Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram.
Mackey TK; Li J; Purushothaman V; Nali M; Shah N; Bardier C; Cai M; Liang B
JMIR Public Health Surveill; 2020 Aug; 6(3):e20794. PubMed ID: 32750006
[TBL] [Abstract][Full Text] [Related]
9. A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States.
Cuomo RE; Purushothaman V; Li J; Cai M; Mackey TK
BMC Public Health; 2021 Apr; 21(1):793. PubMed ID: 33894745
[TBL] [Abstract][Full Text] [Related]
10. Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.
Xu Q; Nali MC; McMann T; Godinez H; Li J; He Y; Cai M; Lee C; Merenda C; Araojo R; Mackey TK
JMIR Infodemiology; 2022; 2(1):e35446. PubMed ID: 37113799
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data.
Benson R; Hu M; Chen AT; Nag S; Zhu SH; Conway M
JMIR Public Health Surveill; 2020 Sep; 6(3):e19975. PubMed ID: 32876579
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. What can we learn about the Ebola outbreak from tweets?
Odlum M; Yoon S
Am J Infect Control; 2015 Jun; 43(6):563-71. PubMed ID: 26042846
[TBL] [Abstract][Full Text] [Related]
16. The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.
Xue J; Chen J; Chen C; Hu R; Zhu T
J Med Internet Res; 2020 Nov; 22(11):e24361. PubMed ID: 33108315
[TBL] [Abstract][Full Text] [Related]
17. Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.
Shen C; Chen A; Luo C; Zhang J; Feng B; Liao W
J Med Internet Res; 2020 May; 22(5):e19421. PubMed ID: 32452804
[TBL] [Abstract][Full Text] [Related]
18. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.
Kagashe I; Yan Z; Suheryani I
J Med Internet Res; 2017 Sep; 19(9):e315. PubMed ID: 28899847
[TBL] [Abstract][Full Text] [Related]
19. Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013.
Mollema L; Harmsen IA; Broekhuizen E; Clijnk R; De Melker H; Paulussen T; Kok G; Ruiter R; Das E
J Med Internet Res; 2015 May; 17(5):e128. PubMed ID: 26013683
[TBL] [Abstract][Full Text] [Related]
20. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.
Kasson E; Singh AK; Huang M; Wu D; Cavazos-Rehg P
Int J Med Inform; 2021 Nov; 155():104574. PubMed ID: 34592539
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
