JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic is a significant public health crisis that negatively affects human health and well-being. As a result of being infected with the Coronavirus, patients can experience long-term health effects, called long COVID. Multiple symptoms characterize this syndrome, and it is crucial to identify these symptoms as they may negatively impact patients’ day-to-day lives. Breathlessness, fatigue, and brain fog are the three main continuing and debilitating symptoms that long COVID patients have reported, often months after the onset of the COVID-19 disease.

Objective:

This study aimed to understand the patterns and behavior of long COVID symptoms, which is vital to improving our understanding of long COVID.

Methods:

Long COVID-19 related Twitter data were collected from 1 May 2020 to 31 December 2021. We used association rule mining techniques to identify frequent symptoms and describe symptom patterns among long COVID patients in Twitter social media discussions.

Results:

The most frequent symptoms in our study included brain fog, fatigue, breathing/lung issues, heart issues, flu symptoms, and depression. General pains, loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in two to six percent of long COVID patients. The highest confidence level-based detection successfully demonstrates the potential of association analysis and Apriori algorithm to establish patterns to detect 62 relationship rules among long COVID symptoms.

Conclusions:

There are very active social media discussions that could support the growing understanding of the COVID-19 and its long-term impact. This enables a potential field of research to analyze the behavior of the long COVID syndrome. Exploratory data analysis was done to identify the symptoms and medical conditions related to long COVID discussions on the Twitter social media platform. Using Apriori algorithm-based association rules, we determined interesting relationships between symptoms.