Socio-semantic dynamics in a blog network

The blogosphere can be construed as a knowledge network made of bloggers who are interacting through a social network to share, exchange or produce information. We claim that the social and semantic dimensions are essentially co-determined and propose to investigate the co-evolutionary dynamics of the blogosphere by examining two intertwined issues: First, how does knowledge distribution drive new interactions and thus influence the social network topology? Second, which role structural network properties play in the information circulation in the system? We adopt an empirical standpoint by analyzing the semantic and social activity of a portion of the US political blogosphere, monitored on a period of four months

Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network

We explore the hypothesis that it is possible to obtain information about the dynamics of a blog network by analysing the temporal relationships between blogs at a semantic level, and that this type of analysis adds to the knowledge that can be extracted by studying the network only at the structural level of URL links. We present an algorithm to automatically detect fine-grained discussion topics, characterized by n-grams and time intervals. We then propose a probabilistic model to estimate the temporal relationships that blogs have with one another. We define the precursor score of blog A in relation to blog B as the probability that A enters a new topic before B, discounting the effect created by asymmetric posting rates. Network-level metrics of precursor and laggard behavior are derived from these dyadic precursor score estimations. This model is used to analyze a network of French political blogs. The scores are compared to traditional link degree metrics. We obtain insights into the dynamics of topic participation on this network, as well as the relationship between precursor/laggard and linking behaviors. We validate and analyze results with the help of an expert on the French blogosphere. Finally, we propose possible applications to the improvement of search engine ranking algorithms

Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network

We explore the hypothesis that it is possible to obtain information about the dynamics of a blog network by analysing the temporal relationships between blogs at a semantic level, and that this type of analysis adds to the knowledge that can be extracted by studying the network only at the structural level of URL links. We present an algorithm to automatically detect fine-grained discussion topics, characterized by n-grams and time intervals. We then propose a probabilistic model to estimate the temporal relationships that blogs have with one another. We define the precursor score of blog A in relation to blog B as the probability that A enters a new topic before B, discounting the effect created by asymmetric posting rates. Network-level metrics of precursor and laggard behavior are derived from these dyadic precursor score estimations. This model is used to analyze a network of French political blogs. The scores are compared to traditional link degree metrics. We obtain insights into the dynamics of topic participation on this network, as well as the relationship between precursor/laggard and linking behaviors. We validate and analyze results with the help of an expert on the French blogosphere. Finally, we propose possible applications to the improvement of search engine ranking algorithms

PLEs from virtual ethnography of social web

This article presents an exploratory research based on the virtual ethnography from an environment of research and learning including new technologies. The ethnography is a method of qualitative research of social sciences that is mainly used in the socio-cultural anthropology, where it has its theoretical basis. The target was to explore the web 2.0 and its tools. The process of participant observation is by means of a blog, other tools and virtual communities. The result is a descriptive model of the web 2.0 based on a Personal Learning Environment which developed in the ethnographic experience.Postprint (published version

A data-driven analysis to question epidemic models for citation cascades on the blogosphere

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.Comment: 18 pages, 9 figures, to be published in ICWSM-13 proceeding

Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201