Trend and co-occurrence network study of symptoms through social media: an example of COVID-19
Authors: Wu Jiageng , Wang Lumin , Hua Yining , Li Minghui , Zhou Li , Bates David W , Yang Jie 
Published: September 2022

Importance

COVID-19 is a multi-organ disease with broad-spectrum manifestations. Clinical data-driven research can be difficult because many patients do not receive prompt diagnoses, treatment, and follow-up studies. Social media’s accessibility, promptness, and rich information provide an opportunity for large-scale and long-term analyses, enabling a comprehensive symptom investigation to complement clinical studies.

Objective

Present an efficient workflow to identify and study the characteristics and co-occurrences of COVID-19 symptoms using social media.

Design, Setting, and Participants

This retrospective cohort study analyzed 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. A comprehensive lexicon of symptoms was used to filter tweets through rule-based methods. 948,478 tweets with self-reported symptoms from 689,551 Twitter users were identified for analysis.

Main Outcomes and Measures

The overall trends of COVID-19 symptoms reported on Twitter were analyzed (separately by the Delta strain and the Omicron strain) using weekly new numbers, overall frequency, and temporal distribution of reported symptoms. A co-occurrence network was developed to investigate relationships between symptoms and affected organ systems.

Results

The weekly quantity of self-reported symptoms has a high consistency (0.8528,P<0.0001) and one-week leading trend (0. 8802,P<0.0001) with new infections in four countries. We grouped 201 common symptoms (mentioned ≥ 10 times) into 10 affected systems. 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 at later stages. When comparing symptoms reported during the Delta strain versus the Omicron variant, significant changes were observed, with dropped odd ratios of coma (95%CI 0.55-0.49,P<0.01) and anosmia (95%CI, 0.6-0.56), and more pain in the throat (95%CI, 1.86-1.96) and concentration problems (95%CI, 1.58-1.70). The co-occurrence network characterizes relationships among symptoms and affected systems, both intra-systemic, such as cough and sneezing (respiratory), and inter-systemic, such as alopecia (integumentary) and impotence (reproductive).

Conclusions and Relevance

We found dynamic COVID-19 symptom evolution through self-reporting on social media and identified 201 symptoms from 10 affected systems. This demonstrates that social media’s prevalence trends and co-occurrence networks can efficiently identify and study public health problems, such as common symptoms during pandemics.

Key points

Questions

What are the epidemic characteristics and relationships of COVID-19 symptoms that have been extensively reported on social media?

Findings

This retrospective cohort study of 948,478 related tweets (February 2020 to April 2022) from 689,551 users identified 201 self-reported COVID-19 symptoms from 10 affected systems, mitigating the potential missing information in hospital-based epidemiologic studies due to many patients not being timely diagnosed and treated. Coma, anosmia, taste sense altered, and dyspnea were less common in participants infected during Omicron prevalence than in Delta. Symptoms that affect the same system have high co-occurrence. Frequent co-occurrences occurred between symptoms and systems corresponding to specific disease progressions, such as palpitations and dyspnea, alopecia and impotence.

Meaning

Trend and network analysis in social media can mine dynamic epidemic characteristics and relationships between symptoms in emergent pandemics.