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Social media has become an integral part of our daily lives. Duringtime-critical events, the public shares a variety of posts on social mediaincluding reports for resource needs, damages, and help offerings for theaffected community. Such posts can be relevant and may contain valuablesituational awareness information. However, the information overload of socialmedia challenges the timely processing and extraction of relevant informationby the emergency services. Furthermore, the growing usage of multimedia contentin the social media posts in recent years further adds to the challenge intimely mining relevant information from social media. In this paper, we presenta novel method for multimodal relevancy classification of social media posts,where relevancy is defined with respect to the information needs of emergencymanagement agencies. Specifically, we experiment with the combination ofsemantic textual features with the image features to efficiently classify arelevant multimodal social media post. We validate our method using anevaluation of classifying the data from three real-world crisis events. Ourexperiments demonstrate that features based on the proposed hybrid framework ofexploiting both textual and image content improve the performance ofidentifying relevant posts. In the light of these experiments, the applicationof the proposed classification method could reduce cognitive load on emergencyservices, in filtering multimodal public posts at large scale.

Relevancy Classification of Multimodal Social Media Streams for Emergency Services