论文标题
在Covid-19中表征药物提及的Twitter聊天
Characterizing drug mentions in COVID-19 Twitter Chatter
论文作者
论文摘要
自从Covid-19分类为全球大流行以来，已经有许多尝试治疗和遏制病毒的尝试。尽管不建议使用Covid-19的特定抗病毒药物治疗，但仍有几种药物可能有助于症状。在这项工作中，我们挖掘了一个大型Twitter数据集，其中包括4.24亿条Covid-19 Chatter的推文，以确定围绕药物提及的论述。虽然似乎是一项简单的任务，但由于Twitter中语言使用的非正式性质，我们证明了机器学习的需求以及传统的自动化方法以帮助完成这项任务。通过应用这些互补方法，我们能够恢复近15％的额外数据，从而使拼写错误处理所需的任务作为处理社交媒体数据时的预处理步骤。
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15% additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.
