Citation cascades in blog networks are often considered as traces of information spreading on this social medium. In this work, we question this point of view using both a structural and semantic analysis of five months activity of the most representative blogs of the french-speaking community.Statistical measures reveal that our dataset shares many features with those that can be found in the literature, suggesting the existence of an identical underlying process. However, a closer analysis of the post content indicates that the popular epidemic-like descriptions of cascades are misleading in this context.A basic model, taking only into account the behavior of bloggers and their restricted social network, accounts for several important statistical features of the data.These arguments support the idea that citations primary goal may not be information spreading on the blogosphere.
 Beyond serving as online diaries, weblogs have evolved into a complex social structure, one which is in many ways ideal for the study of the propagation of information. As weblog authors discover and republish information, we are able to use the existing link structure of blogspace to track its flow. Where the path by which it spreads is ambiguous, we utilize a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection." Our paper describes this technique as well as a visualization system that allows for the graphical tracking of information flow. With an increasing number of people that read, write and comment on blogs, the blogosphere has established itself as an essential medium of communication. A fundamental characteristic of the blogging activity is that bloggers often link to each other. The succession of linking behavior determines the way in which information propagates in the blogosphere, forming cascades. Analyzing cascades can be useful in various applications, such as providing insight of public opinion on various topics and developing better cascade models. This paper presents the results of an excessive study on cascading behavior in the blogosphere. Our objective is to present trends on the degree of engagement and reaction of bloggers in stories that become available in blogs under various parameters and constraints. To this end, we analyze cascades that are attributed to different population groups constrained by factors of gender, age, and continent. We also analyze how cascades differentiate depending on their subject. Our analysis is performed on one of the largest available datasets, including 30M active blogs and 700M posts. The study reveals large variations in the properties of cascades. Can we cluster blogs into types by considering their typical posting and linking behavior? How do blogs evolve over time? In this work we answer these questions, by providing several sets of blog and post features that can help distinguish between blogs. The first two sets of features focus on the topology of the cascades that the blogs are involved in, and the last set of features focuses on the temporal evolution, using chaotic and fractal ideas. We also propose to use PCA to reduce dimensionality, so that we can visualize the resulting clouds of points. We run all our proposed tools on the icwsm dataset. Our findings are that (a) topology features can help us distinguish blogs, like ‘humor’ versus ‘conservative’ blogs (b) the temporal activity of blogs is very non-uniform and bursty but (c) surprisingly often, it is self-similar and thus can be compactly characterized by the so-called bias factor (the ‘80’ in a recursive 80-20 distribution). How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Our goal is to build a model that generates realistic cascades, so that it can help us with link prediction and outlier detection. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Not only are blogs interesting on their own merit, but our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45,000 blogs and 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. We also present a simple model that mimics the spread of information on the blogosphere, and produces information cascades very similar to those found in real life.  We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of WebLogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.  How do blogs produce posts? What local, underlying mechanisms lead to the bursty temporal behaviors observed in blog networks? Earlier work analyzed network patterns of blogs and found that blog behavior is bursty and often follows power laws in both topological and temporal characteristics. However, no intuitive and realistic model has yet been introduced, that can lead to such patterns. This is exactly the focus of this work. We propose a generative model that uses simple and intuitive principles for each individual blog, and yet it is able to produce the temporal characteristics of the blogosphere together with global topological network patterns, like power-laws for degree distributions, for inter-posting times, and several more. Our model ZC uses a novel ‘zero-crossing’ approach based on a random walk, combined with other powerful ideas like exploration and exploitation. This makes it the first model to simultaneously model the topology and temporal dynamics of the blogosphere. We validate our model with experiments on a large collection of 45,000 blogs and 2.2 million posts.