Discovering long COVID symptom patterns: Association rule mining and sentiment analysis in social media tweets (Preprint)

Abstract

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 Syndrome (LCS). 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 most common continuing and debilitating symptoms that long COVID patients have reported, often months after the onset of COVID-19.

This study aimed to understand the patterns and behavior of long COVID symptoms reported by patients on the Twitter social media platform, which is vital to improving our understanding of long COVID.

Long COVID-related Twitter data were collected from 1 May 2020 to 31 December 2021. We used association rule mining (ARM) techniques to identify frequent symptoms and establish relationships between symptoms among long COVID patients in Twitter social media discussions. The highest confidence level-based detection was used to determine the most significant rules with 10% minimum confidence and 0.01% minimum support with a positive lift.

The most frequent symptoms in our study included brain fog (26%), fatigue (17%), breathing/lung issues (16%), heart issues (10%), flu symptoms (9%), depression (7%) and general pains (6%). Loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in two to six percent of long COVID patients. Further, the highest confidence level-based detection successfully demonstrates the potential of association analysis and the apriori algorithm to establish patterns to explore 57 meaningful relationship rules among long COVID symptoms. The strongest relationship revealed that patients with lung/breathing problems and loss of taste are likely to have the loss of smell with 77% confidence.

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 LCS. Exploratory data analysis using Natural Language Processing (NLP) methods revealed 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 and meaningful relationships between symptoms.