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.
The rise of blogs among online social media has been discussed from a sociological point of view in numerous papers, e.g., @cite_8 give some insights about bloggers demographics and cultural behaviors; in @cite_29 , the author describes their influence on society. Given the size and richness of the blog datasets, automatic classification and text-mining tools have been widely used to study the dynamics of trends and opinions in the blogosphere @cite_19 @cite_7 @cite_26 @cite_14 @cite_28 . For example, some studies concentrate on the political blogosphere to understand the ties between political parties, in particular the way information spreads from a group to another @cite_18 @cite_22 . The question of trends is closely related to the definition of authority, influence and trust in social networks @cite_29 @cite_22 . As such, these text-mining tools are also used for various practical purposes as online advertising @cite_5 or search engines, ranking blogs according to their spreading ability @cite_2 .
@cite_18 @cite_26 @cite_14 @cite_22 @cite_7 @cite_8 @cite_28 @cite_29 @cite_19 @cite_2 @cite_5
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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. Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs. 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. 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. 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. 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.