JMIR Publications

ABSTRACT

Background:

A computational framework that utilizes machine learning methodologies was created to collect tweets discussing anthrax, further categorize them as relevant by month of data collection and detect anthrax related events.

Objective:

The objective of this study was to detect anthrax related events and to determine the relevancy of the tweets and topics of discussion over twelve months of data collection.

Methods:

Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was graphed to see if an event was detected (a three-fold spike in tweets). A machine learning classifier was created to categorize tweets as relevant. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how events influence that discussion.

Results:

Over the twelve months of data collection 204,008 tweets were collected. Logistic regression performed best for relevancy (precision=0.81, recall=0.81, and F1-score=0.80). Twenty-six topics were found relating to anthrax events, tweets that were highly re-tweeted, natural outbreaks, and news stories.

Conclusions:

This study demonstrated that tweets relating to anthrax can be collected and analyzed over time to determine what people are discussing and detect key anthrax-related events. Future studies can focus on opinion tweets only, use the methodology to study other terrorism events, or use the methodology to monitor for threats.