  This paper introduces LABurst, a general technique for identifying key
moments, or moments of high impact, in social media streams without the need
for domain-specific information or seed keywords. We leverage machine learning
to model temporal patterns around bursts in Twitter's unfiltered public sample
stream and build a classifier to identify tokens experiencing these bursts. We
show LABurst performs competitively with existing burst detection techniques
while simultaneously providing insight into and detection of unanticipated
moments. To demonstrate our approach's potential, we compare two baseline
event-detection algorithms with our language-agnostic algorithm to detect key
moments across three major sporting competitions: 2013 World Series, 2014 Super
Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series
analysis baseline and is competitive with a domain-specific baseline even
though we operate without any domain knowledge. We then go further by
transferring LABurst's models learned in the sports domain to the task of
identifying earthquakes in Japan and show our method detects large spikes in
earthquake-related tokens within two minutes of the actual event.
