Krishna Yeswanth Kamath

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The emergence of location sharing services is rapidly accelerating the convergence of our online and offline activities. In one direction, Foursquare, Google Latitude, Facebook Places, and related services are enriching real-world venues with the social and semantic connections among online users. In analogy to how clickstreams have been successfully(More)
The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via <i>user-driven linking</i>, in which users annotate their own status updates with lightweight semantic(More)
We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for understanding meme diffusion and information propagation; (ii) examine the spatial propagation of hashtags(More)
In this paper, we study the problem of automatically discovering and tracking <i>transient crowds</i> in highly-dynamic social messaging systems like Twitter and Facebook. Unlike the more static and long-lived group-based membership offered on many social networks (e.g., fan of the LA Lakers), a transient crowd is a short-lived ad-hoc collection of users,(More)
In this paper we seek to understand and model the global spread of social media. How does social media spread from location to location across the globe? Can we model this spread and predict where social media will be popular in the future? Toward answering these questions, we develop a probabilistic model that synthesizes two conflicting hypotheses about(More)
In this paper we describe preliminary approaches for content-based recommendation of Pinterest boards to users. We describe our representation and features for Pinterest boards and users, together with a supervised recommendation model. We observe that features based on latent topics lead to better performance than features based on user-assigned Pinterest(More)
In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed methods model the geo-spatial propagation of online information spread to identify which hashtags will(More)
In this paper, we address the challenge of modeling the size, duration, and temporal dynamics of short-lived crowds that manifest in social media. Successful population modeling for crowds is critical for many services including location recommendation, traffic prediction, and advertising. However, crowd modeling is challenging since 1) user-contributed(More)
Pinterest is a fast-growing interest network with significant user engagement and monetization potential. This paper explores quality signals for Pinterest boards, in particular the notion of board coherence. We find that coherence can be assessed with promising results and we explore its relation to quality signals based on social interaction.