Identifying Eyewitness News-Worthy Events on Twitter

Abstract

In this paper we present a filter for identifying posts from eyewitnesses to various event types on Twitter, including shootings, police activity, and protests. The filter combines sociolinguistic markers and targeted language content with straightforward keywords and regular expressions to yield good accuracy in the returned tweets. Once a set of eyewitness posts in a given semantic context has been produced by the filter, eyewitness events can subsequently be identified by enriching the data with additional geolocation information and then applying a spatio-temporal clustering. By applying these steps we can extract a complete picture of the event as it occurs in real-time, sourced entirely from social media.