This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1456) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1087) Machine Learning (1738) Natural Language Processing (801) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1789) Artificial Intelligence (1739)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47014, first published .
Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Authors of this article:

Elda Kokoe Elolo Laison1 Author Orcid Image ;   Mohamed Hamza Ibrahim2 Author Orcid Image ;   Srikanth Boligarla3 Author Orcid Image ;   Jiaxin Li3 Author Orcid Image ;   Raja Mahadevan3 Author Orcid Image ;   Austen Ng3 Author Orcid Image ;   Venkataraman Muthuramalingam3 Author Orcid Image ;   Wee Yi Lee3 Author Orcid Image ;   Yijun Yin3 Author Orcid Image ;   Bouchra R Nasri1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Wychgram C, Aucott J, Rebman A, Curriero F, Calderaro A. Identifying the geographic leading edge of Lyme disease in the United States with internet searches: A spatiotemporal analysis of Google Health Trends data. PLOS ONE 2024;19(11):e0312277 View
  2. Jia L, Croucher S, Gilbert L. Leveraging natural language processing (NLP) to understand media framing of Lyme disease. Atlantic Journal of Communication 2025:1 View