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.

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.

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.

In this paper we examine the effects of noise when creating a real-world weblog corpus for information retrieval. We focus on the DiffPost (Lee et al. 2008) approach to noise removal from blog pages, examining the difficulties encountered when crawling the blogosphere during the creation of a real-world corpus of blog pages. We introduce and evaluate a number of enhancements to the original DiffPost approach in order to increase the robustness of the algorithm. We then extend DiffPost by looking at the anchor-text to text ratio, and discover that the time-interval between crawls is more important to the successful application of noise-removal algorithms within the blog context, than any additional improvements to the removal algorithm itself.

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semi-supervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.

This paper examines social structures underlying economic activity in Second Life (SL), a massively multiplayer virtual world that allows users to create and trade virtual objects and commodities. We find that users conduct many of their transactions both within their social networks and within groups. Using frequency of chat as a proxy of tie strength, we observe that free items are more likely to be exchanged as the strength of the tie increases. Social ties particularly play a significant role in paid transactions for sellers with a moderately sized customer base. We further find that sellers enjoying repeat business are likely to be selling to niche markets, because their customers tend to be contained in a smaller number of groups. But while social structure and interaction can help explain a seller's revenues and repeat business, they provide little information in the forecasting a seller's future performance. Our quantitative analysis is complemented by a novel method of visualizing the transaction activity of a seller, including revenue, customer base growth, and repeat business.

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.

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.

Vlogs have rapidly evolved from the ’chat from your bedroom’ format to a highly creative form of expression and communication. However, despite the high popularity of vlogging, automatic analysis of conversational vlogs have not been attempted in the literature. In this paper, we present a novel analysis of conversational vlogs based on the characterization of vloggers’ nonverbal behavior. We investigate the use of four nonverbal cues extracted automatically from the audio channel to measure the behavior of vloggers and explore the relation to their degree of popularity and that of their videos. Our study is validated on over 2200 videos and 150 hours of data, and shows that one nonverbal cue (speaking time) is correlated with levels of popularity with a medium size effect.