We present a two-step strategy that addresses fundamental deficiencies
in social media-based event detection and achieves effective
local event by taking advantage of geo-located data from Twitter.
While previous work has mainly relied on an analysis of tweet
text to identify local events, we show how to reliably detect events
using meta-data analysis of geo-tagged tweets. The first step of
the method identifies several spatio-temporal clusters within the
dataset across both space and time using metadata to form potential
candidate events. In the second step, it ranks all the candidates by
the amount of hashtag/entity inequality. We used crowdsourcing
to evaluate the proposed approach on a data set that contains millions
of geo-tagged tweets. The results show that our framework
performs reasonably well in terms of precision and discovers local
events faster.