We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.

We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified.

In many collaborative systems, researchers are interested in creating representative user profiles. In this paper, we are particularly interested in using social labeling and automatic keyword extraction techniques for generating user profiles. Social labeling is a process in which users manually tag other users with keywords. Automatic keyword extraction is a technique that selects the most salient words to represent a user’s contribution. We apply each of these two profile generation methods to highly active Wikipedia editors and their contributions, and compare the results. We found that profiles generated through social labeling matches the profiles generated via automatic keyword extraction, and vice versa. The results suggest that user profiles generated from one method can be used as a seed or bootstrapping proxy for the other method.

We investigate the impact of a discussion snippet's overall sentiment on a user's willingness to read more of a discussion. Using sentiment analysis, we constructed positive, neutral, and negative discussion snippets using the discussion topic and a sample comment from discussions taking place around content on an enterprise social networking site. We computed personalized snippet recommendations for a subset of users and conducted a survey to test how these recommendations were perceived. Our experimental results show that snippets with high sentiments are better discussion "teasers."

In research on blog data, comments are often ignored, What makes a blog post noteworthy? One measure of the and it is easy to see why: comments are very noisy, full popularity or breadth of interest of a blog post is the extent of nonstandard grammar and spelling, usually unedited, often to which readers of the blog are inspired to leave comments cryptic and uninformative, at least to those outside the on the post. In this paper, we study the relationship between blog's community. A few studies have focused on information the text contents of a blog post and the volume of response in comments. Mishe and Glance (2006) showed the it will receive from blog readers. Modeling this relationship value of comments in characterizing the social repercussions has the potential to reveal the interests of a blog's readership of a post, including popularity and controversy. Their largescale community to its authors, readers, advertisers, and scientists user study correlated popularity and comment activity.

This study examined young people’s trust of Wikipedia as an information resource. A large-scale probability-based survey with embedded quasi-experiments was conducted with 2,747 children in the U.S. ranging from 11 to 18 years old. Results show that young people find Wikipedia to be fairly credible, but also exhibit an awareness of potential problems with non-expert, user-generated content in anonymous environments. Children tend to evaluate the credibility of online encyclopedia information with this in mind, at times with what appears to be an unwarranted devaluation of this information.

Online social media often highlight content that is highly rated by neighbors in a social network. For the news aggregator Digg, we use a stochastic model to distinguish the effect of the increased visibility from the network from how interesting content is to users. We find a wide range of interest, and distinguish stories primarily of interest to users in the network from those of more general interest to the user community. This distinction helps predict a story's eventual popularity from users' early reactions to the story.

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. We use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We also compare network properties of our co-participation network to a previously defined reply-answer network on news forums.

In this study, we examine human social interactions within virtual worlds and address the question of how group interactions are affected by the game environment. To investigate this problem, we introduced a set of conversational agents into the social environment of Second Life, a massively multi-player online environment that allows users to construct and inhabit their own 3D world. Our agents were created to be sufficiently lifelike to casual observers, so as not to perturb neighboring social interactions. Using our partitioning algorithm, we separated continuous public chat logs from each region into separate conversations which were used to construct a social network of the participants. Unlike many groups formed in communities and workplaces, groups in Second Life can be rapidly-forming (arising from few interactions), persistent (remaining stable over a long period), and are less affected by socio-cultural influences. In this paper, we analyze regional differences in Second Life by measuring characteristics of the network as a whole, determined from the statistics mined from public conversations in the virtual world, rather than focusing on egocentric actors and their attributes.

Personal photos and their associated metadata reveal different aspects of our lives and, when shared online, let others have an idea about us. Automating the extraction of personal information is an arduous task but it contributes to better understanding and serving users. Here we present methods for analyzing textual metadata associated to Flickr photos that unveil users’ home location and gender. We test our techniques on a sample of 30,000 people coming from six different countries, allowing us to compare results across cultures and point out similarities and differences.