The blogosphere shows the characteristics of a power law distribution where a small set of the bloggers (influentials) get the majority of readership and the vast majority receives little traffic. Blogger recommendation algorithms aim at finding influentials for recommendation, putting bloggers with limited readership at further disadvantage. These bloggers could benefit from mutual endorsement of each other with the eventual goal of forming strong local communities with broader readership. In this paper, we propose a recommendation algorithm to connect blogger pairs with the intent that once connected the bloggers would share a mutually influencing relationship between them. In particular, we compute bloggers' influence profile based on how much she influences her blog friends and recommend bloggers with similar influence profiles. We characterize bloggers into four different groups: global leaders, connectors, local leaders, isolates. Our result shows marginal benefit for isolates and significant benefit for local leaders. Our approach can be instructive in building intelligent recommendation engine for bloggers with limited readership to build strong local communities.

Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.

How does sentiment flow through hyperlink networks? Earlier work on hyperlink networks has focused on the structure of the network, often modeling posts as nodes in a directed graph in which edges represent hyperlinks. At the same time, sentiment analysis has largely focused on classifying texts in isolation. Here we analyze a large hyperlinked network of mass media and weblog posts to determine how sentiment features of a post affect the sentiment of connected posts and the structure of the network itself. We explore the phenomena of sentiment flow through experiments on a graph containing nearly 8 million nodes and 15 million edges. Our analysis indicates that (1) nodes are strongly influenced by their immediate neighbors, (2) deep cascades lead complex but predictable lives, (3) shallow cascades tend to be objective, and (4) sentiment becomes more polarized as depth increases.

We propose a graph-theoretic supervised topic segmentation model for email conversations which combines (i) lexical knowledge, (ii) conversational features, and (iii) topic features. We compare our results with the existing unsupervised models (i.e., LCSeg and LDA), and with their two extensions for email conversations (i.e., LCSeg+FQG and LDA+FQG) that not only use lexical information but also exploit finer conversation structure. Empirical evaluation shows that our supervised model is the best performer and achieves highest accuracy by combining the three different knowledge sources, where knowledge about the conversation has proved to be the most important indicator for segmenting emails.

In this paper we examine the network structure of the MythTV mailing list, an online technology Q&A user community, and we use time-series analysis techniques to study users’ reciprocity behavior in this community. We find that the amount of help users provide is strongly correlated to the amount of help they receive. Further, by conducting the Granger Causality test on the time series data of active users’ activity, we find that the amount of help given is actually the reason why one gets a lot of help. This finding corresponds to the concept of directed reciprocity in social networks and provides insights into social dynamics in technology-centered online communities.

How much do tagging activities tell about a user? Is it possible to identify people in Delicious based on the tags, which they use in Flickr? In this paper we study those questions and investigate whether users can be identified across social tagging systems. We combine two kinds of information: their user ids and their tags. We introduce and compare a variety of approaches to measure the distance between user profiles for identification. With the best performing combination we achieve, depending on the actual settings, accuracies of between 60% and 80% which demonstrates that the traces of Web 2.0 users can reveal quite much about their identity.

Using social media for political discourse is becoming common practice, especially around election time. One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press. Allegedly, predicting electoral outcomes from social media data can be feasible and even simple. Positive results have been reported, but without an analysis on what principle enables them. Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections. Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections. Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports. Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box. Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one.

Online news sites often include a comments section where readers are allowed to leave their thoughts. These comments often contain interesting and insightful conversations between readers about the news article. However the richness of these conversations is often lost among other meaningless comments, and moreover all comments are found at the bottom of the web page. In this article, we discuss how our system inserts reader conversations into the news article to create a multimedia presentation called Shout Out. Shout Out features two virtual news anchors: one anchor reads the news and when appropriate the anchor pauses to have a conversation about the news with another anchor. This current iteration of Shout Out combines natural language techniques and reader conversations to create an engaging system.

In the last years, the blogosphere has become a vital part of the web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. Tracking the public opinion is usually done by conducting surveys resulting in significant costs both for interviewers and persons consulted. In this paper, we propose a method for extracting political trends in the blogosphere.To this end, we apply sentiment and time series analysis techniques in combination with aggregation methods on blog data to estimate the temporal development of opinions on politicians.

Another important question is the absoluteness of the constraints. Lately several web-based tagging systems such as Technorati, Even if we use this approach to turn tags into constraints, Flickr or Delicious have become very popular. In this a fair amount of them are bound to be inaccurate paper we will exploit the information created by the community (i.e., linking documents which should not be in the same in Delicious: a social bookmarking service where cluster) until a high value of the parameter t, due to the polysemy the users can save the URLs of their favourite webpages of the terms used as tags or to differences in the criteria offering also the possibility of associating tags to them. of the taggers. Consequently, we have used soft positive On the other hand the clustering methods are a very important constraints, meaning that the documents affected by one of data mining tool in order to exploit the knowledge them are likely to be in the same cluster, without forcing the present in data collections. In the last years a new family of clustering algorithm to actually put them so.