#Earthquake: Twitter as a Distributed Sensor System

Abstract

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this article we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this with comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the U.S. Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

“…Examples include, for instance, the use of photographs from Flickr for assessing the accuracy of Corine land cover [9] or the use of Twitter for determining whether earthquakes were felt [8]. We Examples include, for instance, the use of photographs from Flickr for assessing the accuracy of Corine land cover [9] or the use of Twitter for determining whether earthquakes were felt [8]. We refer to the righthand side of Figure 1 as "crowdsourced geographic information" as this term covers both Table 1.…”

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

“…As people are increasingly familiar with and ordinarily use social media in their day-to-day life, they naturally tend to uptake these platforms in the occurrence of a disaster for communicating their experience and/or urgent needs. Indeed, in different catastrophic events of the past few years -from the wildfires in Southern California, USA in 2007, over the Earthquake in Haiti in 2010, up to the recent super typhoon in the Philippines 2013 -social media has enabled the affected population to produce information about extreme events and their catastrophic impacts (Sakaki, Okazaki & Matsuo 2010;Crooks et al 2013;De Longueville et al 2010).…”

“…For instance, Sakaki et al (2010) and Crooks et al (2013) investigated the use of Twitter for detecting and estimating the trajectory of earthquakes in real time. De Longueville et al (2010) proposed the use of VGI as a sensor for detecting forest fire hot spots, based on previous work that analysed the application of Twitter as a source of spatiotemporal information for wildfire events in France.…”

“…Bakillah et al (2014) applied graph clustering to support the detection of geolocated communities in Twitter after the typhoon Haiyan in the Philippines. Furthermore, a number of studies are concerned about developing tools for visualising social media data in order to enable make-sensing and location-based knowledge discovery (MacEachren et al 2011;Terpstra & de Vries 2012;Croitoru et al 2013;Spinsanti & Ostermann 2013).…”

“…by defining thresholds for the size of spatiotemporal clusters of messages that would serve as signals for crisis events of earthquakes (Sakaki et al 2010, Crooks et al 2013), wildfires (De Longueville et al 2010, Slavkovikj et al 2014 or disease surveillance (Gomide et al 2011, Bernardo et al 2013). However, with such an approach the actual content of social media messages is largely ignored, and with this, much of their potential to improve the current knowledge about the unfolding situation is lost.…”