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Situational Awareness from Social Media Photographs Using Automated Image Captioning

Following the increasing adoption of online social networks, the users of those services can now easily collect and transmit various types of data instantaneously from real-world locations (e.g., geo-referenced pictures, videos, messages, etc.). This paper argues that one can take advantage of the information that is spread throughout social networks like Twitter, in order to provide a realistic solution for situational awareness that can be useful for decision makers during particular events. Through the generation of English captions assigned to geo-referenced photographs extracted from tweets, and by using these captions to infer important features of those photographs, social monitoring approaches based on the images can produce prompt contextual information that helps in disaster monitoring and response. We specifically describe the use of the Show and Tell model for this purpose. We managed to obtain 100% of precision, 94.42% of accuracy, and 93.93% of recall, in the task of classifying geo-referenced Twitter images depicting the accumulation of snow, useful for applications related to snow removal.