III: EAGER: Discovering Spontaneous Social Events

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Real-world social events provide a convenient and intuitive way to organize social media content for individuals. Current approaches to event detection from social media: assume that the events to be monitored (and their social media signatures) are known a priori; focus largely on text data and fail to take advantage of other forms of media e.g., images. Against this background, this project explores a novel approach to discovering spontaneous, a priori unspecified, social events through joint Bayesian non parametric modeling of multi-modal data (including text and images) and using the events thus discovered to foster new social links. The resulting tools for event discovery will be tested in an application involving discovery of wild animal disease outbreaks from twitter text messages and images posted by individuals. The project brings together an interdisciplinary team of researchers with expertise in image analysis, text mining, and machine learning to advance the state of the art in detection of spontaneous, a priori unspecified events (as they emerge) from social media data. It is expected to yield new scalable nonparametric Bayesian approaches to joint modeling of image and text data, and more generally multi-modal social media data. The resulting tools could potentially transform the way in which people use social media data by empowering them to discover and participate in real world events even as they emerge.