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 capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

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 over 25 text based features, and explore strategies to handle domains that have limited training data. We apply these methods to over 46 million messages spanning 75 companies and 35 languages, from both Facebook and Twitter. The models achieve an aggregate population-weighted F measure of 0.78 and accuracy of 0.74, with values of over 0.9 in some cases.

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 dynamics and message flow cascades, the problem of recommending personalized posting times has remained an under-explored topic of research.

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 Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.

Optical character recognition is carried out using techniques borrowed from statistical machine translation. In particular, the use of multiple simple feature functions in linear combination, along with minimum-error-rate training, integrated decoding, and N-gram language modeling is found to be remarkably effective, across several scripts and languages. Results are presented using both synthetic and real data in five languages.

The Google Books corpus now counts over 15M books spanning 7 centuries and countless languages. Traditional cataloguing at that scale is imprecise, and often fails to identify more complex book-to-book relationships, such as `same text, different pagination' or 'partial overlap'. Our contribution is a two-step technique for clustering books based on content similarity (at both book and page level) and classifying their relationships. We run this on our corpora consisting of more than 15M books (5B pages). We first detect similar books and similar pages within matching books, using hashing techniques and judicious thresholds. We then combine those features to identify the exact relationship between matching books. In this paper, we describe the basic approach to making the problem tractable, as well as the features and classifiers that we used. We enumerate a small number of relationships to qualify the link between scanned real-world books. Finally, we provide precision and recall measurements of the classifier.