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

Media sharing websites and the US financial markets

Recently, one of the main issues of concern within the world wide web is the understanding of web 2.0 mass collaboration systems. These systems have emerged in recent years and gained enormous popularity. It must, however, be pointed out, that the potential and practical application of web 2.0 are still not well understood and deserve academic attention. In this paper we investigate the online media sharing collaborative community and its applications for uses in stock market analysis and prediction. Specifically, we look at Youtube.com, one of the most popular social media sharing websites. The association with stock market behaviour and usage patterns are investigated. This work became of more interest and significance with the recent credit crunch crisis. The data under investigation is novel, and to our knowledge, this paper reports the first investigation of its kind to the use of collaborative media sharing website for stock market analysis. We find significant association between video meta-data and textual data using a content driven sentiment text mining approach. The results are very encouraging and importantly highlight efficient information transfer to online media sharing communities as there seems to be predictive value in youtube data

Are Emotions Enumerable or Decomposable? And its Implications for Emotion Processing

PACLIC 23 / City University of Hong Kong / 3-5 December 200