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For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network(More)
Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at(More)
It has been observed that self-similarity is an emergent property of many real world networks such as WWW, e-mail and biological networks. These networks show properties such as heavy tails for the in-and out-degree distribution, heavy tails for the eigenvalues and eigenvectors, small diameters , and densification and shrinking diameters over time.(More)
Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware, large-scale actionability classification of social media messages. We derive lexicons, perform an in-depth analysis for(More)
Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks—Twitter, Facebook, Google+ and LinkedIn—along with the Wikipedia graph and Internet webpage text and metadata, to rank topical experts across the(More)
In this work, we apply word embeddings and neural networks with Long Short-Term Memory (LSTM) to text classification problems, where the classification criteria are decided by the context of the application. We examine two applications in particular. The first is that of Actionability, where we build models to classify social media messages from customers(More)
In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that(More)
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