Vinodh Krishnan Elangovan

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We present an unsupervised model for inducing signed social networks from the content exchanged across network edges. Inference in this model solves three problems simultaneously: (1) identifying the sign of each edge; (2) characterizing the distribution over content for each edge type; (3) estimating weights for triadic features that map to theoretical(More)
News events and social media are composed of evolving storylines, which capture public attention for a limited period of time. Identifying these story-lines would enable many high-impact applications , such as tracking public interest and opinion in ongoing crisis events. However, this requires integrating temporal and linguistic information, and prior work(More)
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