Boston Marathon Explosions: Analyzing First 1,000 Seconds on Twitter

My colleagues Rumi Chunara and John Brownstein recently published a short co-authored study entitled “Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions.” At 2.49pm EDT on April 15, two improvised bombs exploded near the finish line of the 117th Boston Marathon. Ambulances left the scene approximately 9 minutes later just as public health authorities alerted regional emergency departments of the incident.

Meanwhile, on Twitter:

An analysis of tweets posted within a 35 mile radius of the finish line reveals that the word stems containing “explos*” and “explod*” appeared on Twitter just 3 minutes after the explosions. “While an increase in messages indicating an emergency from a particular location may not make it possible to fully ascertain the circumstances of an incident without computational or human review, analysis of such data could help public safety officers better understand the location or specifics of explosions or other emergencies.”

In terms of geographical coverage, many of the tweets posted during the first 10 minutes were from witnesses in the immediate vicinity of the finish line. “Because of their proximity to the event and content of their postings, these individuals might be witnesses to the bombings or be of close enough proximity to provide helpful information. These finely detailed geographic data can be used to localize and characterize events assisting emergency response in decision-making.”

Ambulances were already on site for the marathon. This is rarely the case for the majority of crises, however. In those more common situations, “crowdsourced information may uniquely provide extremely timely initial recognition of an event and specific clues as to what events may be unfolding.” Of course, user-generated content is not always accurate. Filtering and analyzing this content in real-time is the first step in the verification process, hence the importance of advanced computing. More on this here.

“Additionally, by comparing newly observed data against temporally adjusted keyword frequencies, it is possible to identify aberrant spikes in keyword use. The inclusion of geographical data allows these spikes to be geographically adjusted, as well. Prospective data collection could also harness larger and other streams of crowdsourced data, and use more comprehensive emergency-related keywords and language processing to increase the sensitivity of this data source.” Furthermore, “the analysis of multiple keywords could further improve these prior probabilities by reducing the impact of single false positive keywords derived from benign events.”