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Anthrax on Twitter: Analysis of Public Discussion of Anthrax Over Twelve Months of Data Collection

机译：Twitter上的Anthrax：分析炭疽公众讨论的数据收集十二个月

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Background Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection. Methods This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats.
机译：背景技术社交媒体允许研究人员实时研究意见和对事件的反应。需要更多研究的一个区域是与炭疽相关的事件。创建使用机器学习技术的计算框架来收集讨论炭疽的推文，进一步将它们视为相关的数据收集月份相关，并检测与炭疽病相关事件的讨论。目的本研究的目的是检测对炭疽相关事件的讨论，并确定推文和讨论的主题在12个月内的数据收集中的相关性。方法这是一个Infopeillance研究，在2018年9月25日期，2018年8月25日收集的英语中的推文，从2018年9月25日收集。机器学习技术用于确定人们推文关于炭疽的推文。绘制随时间的数据以确定是否检测到事件（推文中的3倍秒码）。创建机器学习分类器以通过与炭疽相关的相关性进行分类。使用主题建模方法审查相关推文，以确定随时间的讨论主题以及这些事件如何影响该讨论。结果在12个月的数据收集中，收集了共204,008次推文。 Logistic回归分析显示相关性的最佳性能（精确= 0.81;召回= 0.81; F1-得分= 0.80）。总共有26个主题与炭疽病相关的事件有关，推文是高度转发，自然爆发和新闻报道的推文。结论本研究表明，随着时间的推移，可以收集和分析与炭疽相关的推文，以确定人们正在讨论和检测与关键炭疽相关事件的讨论。未来的研究是只关注意见推文，使用方法学习其他恐怖主义事件，或监测恐怖主义威胁。

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