Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.
Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.
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Toward Using Twitter for Tracking COVID-19：A Natural Language Processing Pipeline and Exploratory Data Set.
DOI：
10.2196/25314
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发表时间：
2021-01-22
影响因子：
7.4
通讯作者：
Gonzalez Hernandez G
中科院分区：
文献类型：
--
作者：
Klein AZ;Magge A;O'Connor K;Flores Amaro JI;Weissenbacher D;Gonzalez Hernandez G
在美国，快速发展的COVID-19疫情、可用检测的短缺以及检测结果的延迟为仅基于检测积极监测其传播带来了挑战。这项研究的目的是开发、评估和部署一个自动自然语言处理管道，以收集用户生成的Twitter数据，作为识别美国潜在COVID-19病例的补充资源，这些病例并非基于检测，因此可能没有报告给疾病控制和预防中心。从2020年1月23日开始，我们从Twitter流媒体应用程序编程界面收集了提及COVID-19相关关键词的英文推文。我们应用手写正则表达式来识别表明用户可能已暴露于COVID-19的推文。我们自动从匹配正则表达式的推文中过滤出“报告的语音”（例如，引文、新闻标题），两个注释器注释了随机样本的8976条推文，这些推文带有地理标签或具有配置文件位置元数据，区分了自我报告潜在COVID-19病例的推文和没有自我报告的推文。我们使用带注释的推文来训练和评估基于transformers（BERT）双向编码器表示的深度神经网络分类器。最后，我们在2020年3月1日至8月21日期间持续收集的超过8500万条未标记的推文上部署了自动管道。基于8976条推文中3644条（41%）的双重注释，注释者之间的一致性为0.77（Cohen κ）。基于BERT模型的深度神经网络分类器在与COVID-19相关的推文上进行了预训练，在检测自我报告潜在COVID-19病例的推文时，其F1得分为0.76（精确度=0.76，召回率=0.76）。在部署我们的自动管道后，我们识别出13，714条自我报告潜在COVID-19病例并具有美国州级地理位置的推文。我们已经在这项研究中确定了13，714条推文，沿着每条推文的时间戳和美国州一级的地理位置，公开可供下载。该数据集为未来的工作提供了机会，以评估Twitter数据作为跟踪COVID-19传播的补充资源的效用。
In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out “reported speech” (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state–level geolocations. We have made the 13,714 tweets identified in this study, along with each tweet’s time stamp and US state–level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.
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作者：
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DOI：
10.1093/jamia/ocaa116
发表时间：
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影响因子：
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