Real-time micro-blogging services such as Twitter are widely recognized for their social dynamics--how they both encapsulate a social graph and propagate information across it. However, the content of this information is equally interesting since it frequently reflects individual experiences with a broad variety of real-time events. Indeed, events of broad interest are commonly revealed in correlated spikes of semantically-related posting activity. In this paper, we explore one such application this of phenomenon: using Twitter data to infer on-line Internet service availability. We show that simple techniques are sufficient to extract key semantic content from "tweets" (i.e., service X is down) and also filter out extraneous noise. We demonstrate the efficacy of this approach at identifying a range of large-scale service outages in 2009 for popular services such as Gmail, Bing and PayPal.
['Marti Motoyama', 'Brendan Meeder', 'Kirill Levchenko', 'Geoffrey M. Voelker', 'Stefan Savage']
Measuring online service availability using twitter