Matharaarachchi Surani, Domaratzki Mike, Katz Alan, Muthukumarana Saman
Department of Statistics, University of Manitoba, Winnipeg, MB, Canada.
Department of Computer Science, Western University, London, ON, Canada.
JMIR Form Res. 2022 Sep 7;6(9):e37984. doi: 10.2196/37984.
The COVID-19 pandemic is a substantial 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. 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 3 most common continuing and debilitating symptoms that patients with long COVID 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 May 1, 2020, to December 31, 2021. We used association rule mining techniques to identify frequent symptoms and establish relationships between symptoms among patients with long COVID 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.
Among the 30,327 tweets included in our study, the most frequent symptoms were brain fog (n=7812, 25.8%), fatigue (n=5284, 17.4%), breathing/lung issues (n=4750, 15.7%), heart issues (n=2900, 9.6%), flu symptoms (n=2824, 9.3%), depression (n=2256, 7.4%) and general pains (n=1786, 5.9%). Loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in 1.6% (n=474) to 5.3% (n=1616) of patients with long COVID. Furthermore, 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 a loss of smell with 77% confidence.
There are very active social media discussions that could support the growing understanding of COVID-19 and its long-term impact. These discussions enable a potential field of research to analyze the behavior of long COVID syndrome. Exploratory data analysis using natural language processing 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.
新冠疫情是一场重大的公共卫生危机，对人类健康和福祉产生了负面影响。感染冠状病毒后，患者可能会出现称为“长新冠综合征”的长期健康影响。该综合征有多种症状，识别这些症状至关重要，因为它们可能会对患者的日常生活产生负面影响。呼吸急促、疲劳和脑雾是长新冠患者报告的最常见的持续且使人衰弱的三种症状，通常在新冠疫情发作数月后出现。
本研究旨在了解推特社交媒体平台上患者报告的长新冠症状的模式和表现，这对于增进我们对长新冠的理解至关重要。
收集了2020年5月1日至2021年12月31日期间与长新冠相关的推特数据。我们使用关联规则挖掘技术来识别常见症状，并在推特社交媒体讨论中确定长新冠患者症状之间的关系。基于最高置信度的检测用于确定最显著的规则，最小置信度为10%，最小支持度为0.01%，且具有正向提升度。
在我们研究纳入的30327条推文中，最常见的症状是脑雾（n = 7812，25.8%）、疲劳（n = 5284，17.4%）、呼吸/肺部问题（n = 4750，15.7%）、心脏问题（n = 2900，9.6%）、流感症状（n = 2824，9.3%）、抑郁（n = 2256，7.4%）和全身疼痛（n = 1786，5.9%）。嗅觉和味觉丧失、感冒、咳嗽、胸痛、发烧、头痛和手臂疼痛在1.6%（n = 474）至5.3%（n = 1616）的长新冠患者中出现。此外，基于最高置信度的检测成功证明了关联分析和Apriori算法在建立模式以探索长新冠症状之间57条有意义的关系规则方面的潜力。最强的关系表明，有肺部/呼吸问题和味觉丧失的患者很可能嗅觉丧失，置信度为77%。
社交媒体上有非常活跃的讨论，这有助于增进对新冠疫情及其长期影响的理解。这些讨论为分析长新冠综合征的表现提供了一个潜在的研究领域。使用自然语言处理方法进行的探索性数据分析揭示了推特社交媒体平台上与长新冠讨论相关的症状和健康状况。通过基于Apriori算法的关联规则，我们确定了症状之间有趣且有意义的关系。
