On quality of event localization from social network feeds

Abstract

Social networks, such as Twitter, carry important information on ongoing events and as such can be viewed as networks of sensors that monitor and report events in the physical world. In this paper, we concern ourselves with the challenge of event localization from Twitter feeds. We explore the quality of information that can be derived either directly or indirectly from microblog entries regarding locations of ongoing events. Contrary to prior work that used Twitter to map regions of large-footprint events, or derived coarse-grained location information, in this paper, we are interested in point-events, such as building fires or car accidents, and aim to pin-point them down to a street address. An algorithm is presented that identifies distinct event signatures in the blogosphere, clusters microblogs based on events they describe, and analyzes the resulting clusters for fine-grained location indicators. An exact event location is then derived by fusing these indicators. To evaluate the quality of derived location information, we use road-traffic-related Twitter feeds from 3 major cities in California and compare automatic event localization within our service to manually obtained ground truth data. Results show a great correspondence between our automatically determined locations and ground-truth.