Using request radio shows as a base interactive model, we present the Steerable Optimizing Self-Organized Radio (SoSoRadio) system as a prototypical music rec- ommender system with robust automatic playlist gen- eration. This work describes a web-based radio system that interacts with current listeners through the selection of periodic request songs from a pool of nominees.

Link analysis is a technique frequently used in the ranking of web sites. On the web, we often encounter content that is organized by entries, sorted from recent to old, and generally follows the structure of a blog. In this paper we explore and evaluate the usage of a bibliometrics measure, called h-index, for the task of blog ranking, in an information retrieval context. We base our experiments on the TREC Blogs08 collection, which comprises over 28 million posts. The results obtained indicate that the h-index is a robust metric that allows for an improved relevance discrimination between blogs, when compared to the in-degree. Additionally, tests performed using distinct versions of the post graph, indicate that this metric might tolerate a certain level of link clutter.

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.

Manufacturers of TV sets have recently started adding social media features to their products. Some of these products display microblogging messages relevant to the TV show which the user is currently watching. However, such systems suffer from low precision and recall when they use the title of the show to search for relevant messages. Titles of some popular shows such as Lost or Survivor are highly ambiguous, resulting in messages unrelated to the show. Thus, there is a need to develop filtering algorithms that can achieve both high precision and recall. Filtering microblogging messages for Social TV poses several challenges, including lack of training data, lack of proper grammar and capitalization, lack of context due to text sparsity, etc. We describe a bootstrapping algorithm which uses a small manually labeled dataset, a large dataset of unlabeled messages, and some domain knowledge to derive a high precision classifier that can successfully filter microblogging messages which discuss television shows. The classifier is designed to generalize to TV shows which were not part of the training set. The algorithm achieves high precision on our two test datasets and successfully generalizes to unseen television shows. Furthermore, it compares favorably to a text classifier specifically trained on the television shows used for testing.

User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.

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.

We consider prominence ranking in graphs involving actors, their artifacts and the artifact groups. When multiple actors contributing to an artifact constitutes a social tie, associations between the artifacts can be used to infer prominence among actors. This is because prominent actors will tend to collaborate on prominent artifacts, and prominent artifacts will be associated with other prominent artifacts. Our testbed example is the DBLP co-authorship graph: multiple authors (the actors) collaborate to publish research papers (the artifacts); collaboration is the social tie. Papers have prominence themselves (eg. quality and impact of the work) and the prominence of the venues are tied to the prominence of the papers in them. We use our methods to infer prominence based on the venue-based associations of papers, and compare our rankings with external citation based measures of prominence. We compare with numerous other ranking algorithms, and show that the ranking performance gain from using the venues is statistically significant. What if there are no natural artifact groups like venues? We develop a new algorithm which uses discovered artifact groups. Our approach consists of two steps. First, we find artifact groups by linking artifacts with common contributors. Note that instead of finding communities of actors, we consider communities of artifacts. We then use these grouped artifacts in the prominence ranking algorithm. We consider different methods for obtaining the artifact groups, in particular a very efficient embedding based algorithm for graph clustering and show the effectiveness of our method in improving the ranking of actors. The inferred groups are as good as or better than the natural conference venues for DBLP.

Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0% accuracy on articles.

Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems.

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.