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.

SL also makes it easy to The past few years have seen a rise in number and popularity meet and interact with new people. of online spaces where individuals can socialize, play, 4. Transaction: Creating content or providing services in SL and learn. All of these spaces face the challenge of retaining can be profitable, with 150M USD in user-to-user transactions the interest of users over time. We study this problem in taking place in the third quarter of 2009 (Linden the context of Second Life (SL).

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.

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.

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.

The Web has become an important information repository; often it is the first source a person turns to with an informa-tion need. One common way to search the Web is with a search engine. However, it is not always easy for people to find what they are looking for with keyword search, and at times the desired information may not be readily available online. An alternative, facilitated by the rise of social media, is to pose a question to one‟s online social network. In this paper, we explore the pros and cons of using a social net-working tool to fill an information need, as compared with a search engine. We describe a study in which 12 participants searched the Web while simultaneously posing a question on the same topic to their social network, and we compare the results they found by each method.

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r.t. features which may be easily removed by spammers.

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

Given a set of reviews of products or merchants from a wide range of authors and several reviews websites, how can we measure the true quality of the product or merchant?  How do we remove the bias of individual authors or sources?  How do we compare reviews obtained from different websites, where ratings may be on different scales (1-5 stars, A/B/C, etc.)?  How do we filter out unreliable reviews to use only the ones with ``star quality''?  Taking into account these considerations, we analyze data sets from a variety of different reviews sites (the first paper, to our knowledge, to do this). These data sets include 8 million product reviews and 1.5 million merchant reviews. We explore statistic- and heuristic- based models for estimating the true quality of a product or merchant, and compare the performance of these estimators on the task of ranking pairs of objects.  We also apply the same models to the task of using Netflix ratings data to rank pairs of movies, and discover that the performance of the different models is surprisingly similar on this data set.