Social media has become an important open communication medium during crises. This has motivated much work on social media data analysis for crises situations using machine learning techniques but has mostly been carried out by traditional techniques. Those methods have shown mixed results and are criticised for being unable to generalize beyond the scope of the designed study. Since every crisis is special, such retrospect models have little value. In contrast, deep learning shows very promising results by learning in noisy environments such as image classification and game playing. It has, therefore great potential to play a significant role in the future social media analysis in noisy crises situations. This position paper proposes an approach to improve the social media analysis in crises situations to achieve better understanding and decision support during a crisis. In this approach, we aim to use Deep Learning to extract features and patterns related to the text and concepts available in crisis related social media posts and use them to provide an overview of the crisis.
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