Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing

Area of Science:

• Computational epidemiology
• Medical informatics
• Natural Language Processing

Background:

• Social media platforms offer vast, real-time data for public health monitoring.
• Tracking COVID-19 cases and symptom prevalence is crucial for understanding disease dynamics.

Purpose of the Study:

• To develop and validate NLP models for identifying COVID-19 cases and extracting symptoms from social media data.
• To analyze changes in symptom prevalence across different COVID-19 variant periods (early, Delta, Omicron).

Main Methods:

• Utilized social media data from the "covid19positive" subreddit (03/2020-03/2022).
• Trained a Bidirectional Encoder Representations from Transformers (BERT) classification model for case identification (91.2% accuracy).
• Developed a novel QuadArm model (Question-Answering, dual-corpus expansion, Adaptive rotation clustering, mapping) for symptom extraction.

Main Results:

• Identified 310, 8794, and 12,094 COVID-positive authors during the early, Delta, and Omicron periods, respectively.
• Common early symptoms included coughing, fever, loss of smell, headache, and sore throat.
• Symptom reporting decreased during Delta, with reduced loss of smell and increased sore throat during Omicron.

Conclusions:

• NLP models can accurately identify COVID-19 cases and efficiently extract symptom data from social media.
• This approach provides valuable insights into evolving symptom patterns of COVID-19 variants.
• Social media analysis is a viable tool for real-time public health surveillance.