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Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the global network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks with cascade migration to a new locality as well as predict locality saturation from interretweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.

Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network, thus benefiting diverse applications including viral marketing, disease control and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and hence act as precursors for detection of influential nodes 1. The set of influential nodes identified by SmartInf have the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter show the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.

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