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

TL;DR

This paper introduces a semantic temporal analysis method for blog networks, using an algorithm and probabilistic model to identify precursor and laggard behaviors, providing insights beyond traditional link analysis.

Contribution

It presents a novel algorithm and probabilistic model to analyze semantic temporal relationships and behaviors in blog networks, enhancing understanding of information dynamics.

Findings

Identified precursor and laggard behaviors in political blogs

Compared semantic temporal metrics with link-based metrics

Provided insights into topic participation dynamics

Abstract

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.