We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 4000 topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of all the tweets posted by these users between June 2009 and August 2009 (approximately 200 million tweets), we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.
Leskovec, Backstrom and Kleinberg's seminal work on the evolution of topics in the news sphere was the starting point for this paper @cite_11 . They studied how the growth of one topic affects the growth of other topics in the blogosphere. They identified and tracked a small number of popular threads, and showed that the growth of the number of posts on a thread negatively impacts the growth of other threads. The basic question that arose on reading that work was this: Can the nuances of the temporal evolution of topics be explained by a more thorough study of their spatial evolution? Working with a data set taken from Twitter we were able to extract the high level of structural and geographical information about the actors of the process that has allowed us to answer this question in the affirmative. This allows us to challenge the line of research that studies only the temporal evolution of topics @cite_6 , or seeks to explain this evolution on the basis of content @cite_14 .
@cite_14 @cite_6 @cite_11
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Human-generated textual data streams from services such as Twitter increasingly become geo-referenced. The spatial resolution of their coverage improves quickly, making them a promising instrument for sensing various aspects of evolution and dynamics of social systems. This work explores spacetime structures of the topical content of short textual messages in a stream available from Twitter in Ireland. It uses a streaming Latent Dirichlet Allocation topic model trained with an incremental variational Bayes method. The posterior probabilities of the discovered topics are post-processed with a spatial kernel density and subjected to comparative analysis. The identified prevailing topics are often found to be spatially contiguous. We apply Markov-modulated non-homogeneous Poisson processes to quantify a proportion of novelty in the observed abnormal patterns. A combined use of these techniques allows for real-time analysis of the temporal evolution and spatial variability of population's response to various stimuli such as large scale sportive, political or cultural events. In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus. The topic evolution graphs obtained from the ACM corpus provide an effective and concrete summary of the corpus with remarkably rich topology that are congruent to our background knowledge. In a finer resolution, the graphs reveal concrete information about the corpus that were previously unknown to us, suggesting the utility of our approach as a navigational tool for the corpus. We propose two new tools to address the evolution of hyperlinked corpora. First, we define time graphs to extend the traditional notion of an evolving directed graph, capturing link creation as a point phenomenon in time. Second, we develop definitions and algorithms for time-dense community tracking, to crystallize the notion of community evolution. We develop these tools in the context of Blogspace , the space of weblogs (or blogs). Our study involves approximately 750K links among 25K blogs. We create a time graph on these blogs by an automatic analysis of their internal time stamps. We then study the evolution of connected component structure and microscopic community structure in this time graph. We show that Blogspace underwent a transition behavior around the end of 2001, and has been rapidly expanding over the past year, not just in metrics of scale, but also in metrics of community structure and connectedness. This expansion shows no sign of abating, although measures of connectedness must plateau within two years. By randomizing link destinations in Blogspace, but retaining sources and timestamps, we introduce a concept of randomized Blogspace . Herein, we observe similar evolution of a giant component, but no corresponding increase in community structure. Having demonstrated the formation of micro-communities over time, we then turn to the ongoing activity within active communities. We extend recent work of Kleinberg [11] to discover dense periods of "bursty" intra-community link creation. User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time. A good example is a web-blogging community with the daily blog posts and a social network of bloggers. An important task of analyzing an online community is to observe and track the popular events, or topics that evolve over time in the community. Existing approaches usually focus on either the burstiness of topics or the evolution of networks, but ignoring the interplay between textual topics and network structures. In this paper, we formally define the problem of popular event tracking in online communities (PET), focusing on the interplay between texts and networks. We propose a novel statistical method that models the the popularity of events over time, taking into consideration the burstiness of user interest, information diffusion on the network structure, and the evolution of textual topics. Specifically, a Gibbs Random Field is defined to model the influence of historic status and the dependency relationships in the graph; thereafter a topic model generates the words in text content of the event, regularized by the Gibbs Random Field. We prove that two classic models in information diffusion and text burstiness are special cases of our model under certain situations. Empirical experiments with two different communities and datasets (i.e., Twitter and DBLP) show that our approach is effective and outperforms existing approaches.