Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.

ABSTRACT:

Background

While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times.

Objective

Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19-related drugs.

Methods

This study constructed a full pipeline for COVID-19-related drug tweet analysis, using pretrained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of

SUBMITTER: Li W 

PROVIDER: S-EPMC11923463 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature