Flood Detection via Twitter Streams using Textual and Visual Features

• URL: http://arxiv.org/abs/2011.14944v1
• Date: Mon, 30 Nov 2020 16:09:11 GMT
• Title: Flood Detection via Twitter Streams using Textual and Visual Features
• Authors: Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci, Ala AL-Fuqaha
• Abstract summary: The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task. The task aims to analyze and detect flooding events in multimedia content shared over Twitter.
• Score: 5.615972945389011
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter. In total, we proposed four different solutions including a multi-modal solution combining textual and visual information for the mandatory run, and three single modal image and text-based solutions as optional runs. In the multimodal method, we rely on a supervised multimodal bitransformer model that combines textual and visual features in an early fusion, achieving a micro F1-score of .859 on the development data set. For the text-based flood events detection, we use a transformer network (i.e., pretrained Italian BERT model) achieving an F1-score of .853. For image-based solutions, we employed multiple deep models, pre-trained on both, the ImageNet and places data sets, individually and combined in an early fusion achieving F1-scores of .816 and .805 on the development set, respectively.

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