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Within this article we analyze the spatial and temporal characteristics of the <jats:italic>twitter<\/jats:italic> feed activity responding to a 5.8 magnitude earthquake which occurred on the <jats:styled-content style=\"fixed-case\">E<\/jats:styled-content>ast <jats:styled-content style=\"fixed-case\">C<\/jats:styled-content>oast of the <jats:styled-content style=\"fixed-case\">U<\/jats:styled-content>nited <jats:styled-content style=\"fixed-case\">S<\/jats:styled-content>tates (<jats:styled-content style=\"fixed-case\">US<\/jats:styled-content>) on <jats:styled-content style=\"fixed-case\">A<\/jats:styled-content>ugust 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 \u2018Did You Feel It?\u2019 (<jats:styled-content style=\"fixed-case\">DYFI<\/jats:styled-content>) website of the <jats:styled-content style=\"fixed-case\">U<\/jats:styled-content>.<jats:styled-content style=\"fixed-case\">S<\/jats:styled-content>. 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.<\/jats:p>","DOI":"10.1111\/j.1467-9671.2012.01359.x","type":"journal-article","created":{"date-parts":[[2012,10,9]],"date-time":"2012-10-09T04:31:51Z","timestamp":1349757111000},"page":"124-147","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":345,"title":["#Earthquake: Twitter as a Distributed Sensor System"],"prefix":"10.1111","volume":"17","author":[{"given":"Andrew","family":"Crooks","sequence":"first","affiliation":[{"name":"Department of Computational Social Science George Mason University"}]},{"given":"Arie","family":"Croitoru","sequence":"additional","affiliation":[{"name":"Center for Geospatial Intelligence Department of Geography and Geoinformation Science George Mason University"}]},{"given":"Anthony","family":"Stefanidis","sequence":"additional","affiliation":[{"name":"Center for Geospatial Intelligence Department of Geography and Geoinformation Science George Mason University"}]},{"given":"Jacek","family":"Radzikowski","sequence":"additional","affiliation":[{"name":"Center for Geospatial Intelligence Department of Geography and Geoinformation Science George Mason University"}]}],"member":"311","published-online":{"date-parts":[[2012,10,9]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"AbadiD MaddenS andLindnerW2005REED: Robust Efficient Filtering and Event Detection in sensor networks. 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