In this paper we present several methods for collecting Web textual contents and filtering noisy data. We show that knowing which user publishes which contents can contribute to detecting noise. We begin by collecting data from two forums and from Twitter. For the forums, we extract the meaningful information from each discussion (texts of question and answers, IDs of users, date). For the Twitter dataset, we first detect tweets with very similar texts, which helps avoiding redundancy in further analysis. Also, this leads us to clusters of tweets that can be used in the same way as the forum discussions: they can be modeled by bipartite graphs. The analysis of nodes of the resulting graphs shows that network structure and content type (noisy or relevant) are not independent, so network studying can help in filtering noise.

The emergence of online platforms allowing to mix self publishing activities and social networking offers new possibilities for building online reputation and visibility. In this paper we present a method to analyze the online popularity that takes into consideration both the success of the published content and the social network topology. First, we adapt the Kohonen self organizing maps in order to cluster the users of online platforms depending on their audience and authority characteristics. Then, we perform a detailed analysis of the manner nodes are organized in the social network. Finally, we study the relationship between the network local structure around each node and the corresponding user’s popularity. We apply this method to the MySpace music social network. We observe that the most popular artists are centers of star shaped social structures and that it exists a fraction of artists who are involved in community and social activity dynamics independently of their popularity. This method based on a learning algorithm and on network analysis appears to be a robust and intuitive technique for a rich description of the online behavior.