Patterns of Cascading Behavior in Large Blog Graphs

Abstract

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.

Impact of heterogeneous human activities on epidemic spreading

“…Examples include the email communication [22], the cell-phone communication [23], the short-message communication [24,25,26], the web page visits [27,28,29] and some other online activities [30,31]. To name just a few.…”

Identifying Transformative Scientific Research

“…Various models are available to explain and predict information diffusion [15], [36], [37], [38]. Widely spread information may be disrupted by the presence of another piece of information that competes to gain attention from information consumers [39].…”

Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis

“…Of most relevance to this paper is perhaps the rich literature on the general explorative mining of the blogosphere (see, e.g., Joshi et al 2007; Leskovec et al 2007b;Agarwal and Liu 2008;Hearst and Dumais 2009). Much has been said about the the underlying topology (Shi et al 2007), demographics (Kumar et al 2003), structure (Kumar et al 2006) and evolution (Kumar et al 2003;Backstrom et al 2006; Kumar et al 2006;Leskovec et al 2007a;Götz et al 2009) of the blogosphere.…”

“…The methodology used is generally one of understanding information cascades (Leskovec et al 2007b;Papagelis et al 2009;Ghosh and Lerman 2011). In other words, what are the specific patterns of diffusion for specific pieces of information.…”

“…However, as the data have become increasingly available over recent years, researchers in computer science, physics, social sciences, and more have started to analyze these networks to identify network characteristics, communities, behaviors, influencers, etc. (see, e.g., Adamic and Glance 2005;Leskovec et al 2007b;Joshi et al 2007;Nallapati and Cohen 2008;Agarwal and Liu 2008;Hearst and Dumais 2009).…”