Identifying Eyewitness News-Worthy Events on Twitter

摘要

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 sociolin-guistic 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.