Classification of Twitter Users Who Tweet About E-Cigarettes.
Classification of Twitter Users Who Tweet About E-Cigarettes.
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DOI：
10.2196/publichealth.8060
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发表时间：
2017-09-26
影响因子：
8.5
通讯作者：
Nonnemaker J
中科院分区：
文献类型：
--
作者：
Kim A;Miano T;Chew R;Eggers M;Nonnemaker J
尽管人们担心电子烟的健康风险，但近年来电子烟越来越受欢迎。随着最近电子烟使用的增加，Twitter等社交媒体网站已成为分享电子烟信息和促进电子烟营销的通用平台。要监测电子邮件相关社交媒体活动的趋势，就需要及时评估帖子的内容和生成内容的用户类型。然而，人们对Twitter上负责生成电子邮件相关内容的用户类型的多样性知之甚少。这项研究的目的是展示一种新的方法，用于自动将关于电子邮件相关主题的Twitter用户分类为不同的类别。我们收集了2014年11月至2016年10月期间发布的约1150万条与电子烟相关的推文，并随机抽取了一些发布电子烟相关推文的Twitter用户。经过培训的人类编码员检查了手柄的配置文件，并将每个用户手动分类为以下用户类型之一：个人（n=2168），vaper爱好者（n=334），知情机构（n=622），营销人员（n=752）和垃圾邮件发送者（n=1021）。接下来，收集Twitter元数据以及每个标记用户的推文样本，并分析反映用户元数据和推文行为的特征。最后，测试了多种机器学习算法，以确定在分类用户类型方面具有最佳性能的模型。使用一个包括元数据和与推文行为相关的特征的分类模型，我们能够以相对较高的准确度预测五种不同类型的推文用户（平均F1得分=83.3%）。准确性因用户类型而异，个人，知情机构，营销人员，垃圾邮件发送者和电子烟爱好者的F1得分分别为91.1%，84.4%，81.2%，79.5%和47.1%。Vaper爱好者是最具挑战性的用户类型，准确预测，通常被错误地归类为营销人员。包含额外的tweet-derived功能，捕捉tweeting行为被发现显着提高模型的性能-一个整体的F1得分增益10.6%-超越元数据功能。这项研究提供了一种方法，可以对五种不同类型的电子烟用户进行分类。我们的模型对大多数群体都实现了高水平的分类性能，并且检查推文行为对于提高模型性能至关重要。结果可以帮助识别参与在线电子烟对话的群体，以帮助为公共卫生监督，教育和监管工作提供信息。
Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette–related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette–related content on Twitter. The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette–related topics into distinct categories. We collected approximately 11.5 million e‑cigarette–related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles’ profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users’ metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance—an overall F1 score gain of 10.6%—beyond metadata features alone. This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.