Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events.

Michele Miller,William Romine,Terry Oroszi

doi:10.2196/27976

Abstract

BackgroundSocial 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.ObjectiveThe 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.MethodsThis 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.ResultsOver 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.ConclusionsThis 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.

Highlights

Terrorism can be traced back to almost 2000 years ago when the Sicarii-Zealots, a Jewish resistance group, used assassins to stab Roman legionnaires or Jewish collaborators, use poison, or kidnap Temple Guard staff for ransom [1]
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
20 events were detected over the course of data collection (Figure 2), of which 6 concerned current anthrax-related events, 3 were about North Korea having access to anthrax, 3 were about anthrax scares, and 5 were related to the former director of the US Federal Bureau of Investigation (FBI) Robert Mueller, who oversaw the FBI during the anthrax attacks and was in charge of investigating the collusion with Russia during data collection

Summary

Introduction

Background Terrorism can be traced back to almost 2000 years ago when the Sicarii-Zealots, a Jewish resistance group, used assassins to stab Roman legionnaires or Jewish collaborators, use poison, or kidnap Temple Guard staff for ransom [1]. The fear resulting from terrorism typically causes more damage to our economy and social fabric than the act of terror itself. In addition to neutralizing terror attacks before they occur, efforts to combat https://publichealth.jmir.org/2021/6/e27976 XSLFO RenderX. 1 (page number not for citation purposes) terrorism are focused on minimizing negative social after-effects of attacks that do occur. To this end, it is imperative to identify and address fears and misconceptions to mitigate the additional damage. 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

Methods

Results

Discussion

Conclusion