  In this contribution, we develop an accurate and effective event detection
method to detect events from a Twitter stream, which uses visual and textual
information to improve the performance of the mining process. The method
monitors a Twitter stream to pick up tweets having texts and images and stores
them into a database. This is followed by applying a mining algorithm to detect
an event. The procedure starts with detecting events based on text only by
using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Then it detects the event
based on image only by using visual features including histogram of oriented
gradients (HOG) descriptors, grey-level cooccurrence matrix (GLCM), and color
histogram. K nearest neighbours (Knn) classification is used in the detection.
The final decision of the event detection is made based on the reliabilities of
text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.94, comparing with 0.89 with
texts only, and 0.86 with images only.
