Chapters authored
Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets
A method for detecting hot events such as wildfires is proposed. It uses visual and textual information to improve detection. Starting with picking up tweets having texts and images, it preprocesses the data to eliminate unwanted data, transforms unstructured data into structured data, then extracts features. Text features include term frequency-inverse document frequency. Image features include histogram of oriented gradients, gray-level co-occurrence matrix, color histogram, and scale-invariant feature transform. Next, it inputs the features to the multiple kernel learning (MKL) for fusion to automatically combine both feature types to achieve the best performance. Finally, it does event detection. The method was tested on Brisbane hailstorm 2014 and California wildfires 2017. It was compared with methods that used text only or images only. With the Brisbane hailstorm data, the proposed method achieved the best performance, with a fusion accuracy of 0.93, comparing to 0.89 with text only, and 0.85 with images only. With the California wildfires data, a similar performance was recorded. It has demonstrated that event detection in Twitter is enhanced and improved by combination of multiple features. It has delivered an accurate and effective event detection method for spreading awareness and organizing responses, leading to better disaster management.
Part of the book: Machine Learning