  Events detected from social media streams often include early signs of
accidents, crimes or disasters. Therefore, they can be used by related parties
for timely and efficient response. Although significant progress has been made
on event detection from tweet streams, most existing methods have not
considered the posted images in tweets, which provide richer information than
the text, and potentially can be a reliable indicator of whether an event
occurs or not. In this paper, we design an event detection algorithm that
combines textual, statistical and image information, following an unsupervised
machine learning approach. Specifically, the algorithm starts with semantic and
statistical analyses to obtain a list of tweet clusters, each of which
corresponds to an event candidate, and then performs image analysis to separate
events from non-events---a convolutional autoencoder is trained for each
cluster as an anomaly detector, where a part of the images are used as the
training data and the remaining images are used as the test instances. Our
experiments on multiple datasets verify that when an event occurs, the mean
reconstruction errors of the training and test images are much closer, compared
with the case where the candidate is a non-event cluster. Based on this
finding, the algorithm rejects a candidate if the difference is larger than a
threshold. Experimental results over millions of tweets demonstrate that this
image analysis enhanced approach can significantly increase the precision with
minimum impact on the recall.
