Paper: http://ceur-ws.org/Vol-2882/paper19.pdf
Rabiul Islam Jony and Alan Woodley : Flood Detection in Twitter Using a Novel Learning Method for Neural Networks. Proc. of MediaEval 2020, 14-15 December 2020, Online.
In this paper we use a novel backpropagation technique, Direct Backpropagation (DBP) to train a neural network and use it to detect flooding in Twitter posts. We use the the textual information from the tweets and the visual features form the associated images to classify the posts into two categories, flood (1) and no-flood (0). We also fuse these two modes using fusion methods for the classification. For the classification task we employ a neural network that we train using our proposed method instead of typical backpropgation method. This work has been done in the context of the MediaEval 2020 Flood-Related Multimedia Task.
Flood Detection in Twitter Using a Novel Learning Method for Neural Networks
1. Flood Detection in Twitter Using a
Novel Learning Method for
Neural Networks
Rabiul Islam Jony1, Alan Woodley2
School of Computer Science, Science and Engineering Faculty1,2
Queensland University of Technology
ARC Centre of Excellence for Mathematical & Statistical Frontiers2
Brisbane, Australia
{r.jony, a.woodley}@qut.edu.au
3. Textual Information
• Textual information
• Full text
• Tags
• BoW, tf-idf, uni-gram and bi-gram
• Feature reduction
• Chi-squared method
• 2048 features
6. Direct Backpropagation (DBP)
• δa2 and δa1 are the second and first layer gradients respectively
• f’() is the derivative of the non-linearity
• e is the cost function
• W and WT are forward weight and backward transpose weighs
respectively
BP= Backpropagation
DBP= Direct backpropagation
Rabiul Islam Jony, Alan Woodley, and Dimitri Perrin. 2020. Fusing Visual Features and Metadata to Detect Flooding in Flickr Images. In 2020 Digital Image Computing: Techniques
and Applications (DICTA).
