  The detection of events from online social networks is a recent, evolving
field that attracts researchers from across a spectrum of disciplines and
domains. Here we report a time-series analysis for predicting events. In
particular, we evaluated the frequency distribution of top n-grams of terms
over time, focusing on two indicators: high-frequency n-grams over both short
and long periods of time. Both indicators can refer to certain aspects of
events as they evolve. To evaluate the models accuracy in detecting events, we
built and used a Twitter dataset of the most popular hashtags that surrounded
the well-documented protests that occurred at the University of Missouri
(Mizzou) in late 2015.
