论文标题
使用基于上下文的Twitter嵌入来检测COVID-19的新兴症状
Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
论文作者
论文摘要
在本文中，我们提出了一种基于迭代图的方法，用于检测COVID-19的症状，其病理似乎正在发展。更一般而言，该方法可以应用于在大型不平衡的语料库中查找上下文特定的单词和文本（例如症状提及）（例如，所有提及＃covid-19的推文）。鉴于Covid-19的新颖性，我们还测试了所提出的方法是否概括了检测不良药物反应（ADR）的问题。我们发现，应用于Twitter数据的方法可以在疾病控制中心（CDC）报告之前大量检测症状。
In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can be applied to finding context-specific words and texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets mentioning #COVID-19). Given the novelty of COVID-19, we also test if the proposed approach generalizes to the problem of detecting Adverse Drug Reaction (ADR). We find that the approach applied to Twitter data can detect symptom mentions substantially before being reported by the Centers for Disease Control (CDC).
