Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study

Abstract

Background: For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research.

Objective: Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data.

Methods: This retrospective study included 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems.

Results: This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive).

Conclusions: This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.

Keywords: COVID-19; data mining; network analysis; public health; social media.