Flood Detection in Twitter Using a Novel Learning Method for Neural Networks.

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 textual information from the tweets and the visual features from 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 backpropagation method. This work has been done in the context of the MediaEval 2020 Flood-Related Multimedia Task.