  Streams of user-generated content in social media exhibit patterns of
collective attention across diverse topics, with temporal structures determined
both by exogenous factors and endogenous factors. Teasing apart different
topics and resolving their individual, concurrent, activity timelines is a key
challenge in extracting knowledge from microblog streams. Facing this challenge
requires the use of methods that expose latent signals by using term
correlations across posts and over time. Here we focus on content posted to
Twitter during the London 2012 Olympics, for which a detailed schedule of
events is independently available and can be used for reference. We mine the
temporal structure of topical activity by using two methods based on
non-negative matrix factorization. We show that for events in the Olympics
schedule that can be semantically matched to Twitter topics, the extracted
Twitter activity timeline closely matches the known timeline from the schedule.
Our results show that, given appropriate techniques to detect latent signals,
Twitter can be used as a social sensor to extract topical-temporal information
on real-world events at high temporal resolution.
