Most of the popular multimedia sharing web-sites such as YouTube, Flickr etc not only allow users to author and upload content but also facilitate "social" networking amongst users. These social interactions can be in the form of - user-to-user interactions i.e. adding existing users to friend or contact list or user-to-content interactions : commenting on a video or picture, marking a picture/video as "favorite", subscribing to a user created "channel" etc. Analyzing these social interactions jointly with the content metadata (such as the description of the video, keywords associated with the image/video etc) can reveal interesting insights about user activity on these social media platforms. In this paper, we propose an unsupervised method that jointly models "social" interaction and content metadata in YouTube to discover user-communities and the nature of topics beings discussed in these communities. We report the effectiveness of the proposed method on real-world dataset.

Microblogging services like Twitter are used for a wide variety of purposes and in different modes. Here, we focus on the usage of Twitter for "chatter" i.e., the production and consumption of tweets that are typically non-topical and contain personal status updates or conversational messages which are usually intended and are useful only to the immediate network of the producers of the tweets. The automatic identification of chatter tweets is critical for tasks such as ranking tweets by relevance, matching tweets to advertisements, creation of topical digests of tweets, etc. and generally improves the utility of tweets to people outside the producers' immediate network by enabling the filtering out of tweets that are not of wider interest. We study the prevalence of chatter tweets in Twitter and present techniques to detect them using machine learning techniques that require minimal supervision.

We present a pseudo-observed variable based regularization technique for latent variable mixed-membership models that provides a mechanism to impose preferences on the characteristics of aggregate functions of latent and observed variables. The regularization framework is used to regularize topic models, which are latent variable mixed membership models for language modeling. In many domains, documents and words often exhibit only a slight degree of mixed-membership behavior that is inadequately modeled by topic models which are overly liberal in permitting mixed-membership behavior. The regularization introduced in the paper is used to control the degree of polysemy of words permitted by topic models and to prefer sparsity in topic distributions of documents in a manner that is much more flexible than permitted by modification of priors. The utility of the regularization in exploiting sentiment-indicative features is evaluated internally using document perplexity and externally by using the models to predict star counts in movie and product reviews based on the content of the reviews. Results of our experiments show that using the regularization to finely control the behavior of topic models leads to better perplexity and lower mean squared error rates in the star-prediction task.

Identifying latent groups of entities from observed interactions between pairs of entities is a frequently encountered problem in areas like analysis of protein interactions and social networks. We present a model that combines aspects of mixed membership stochastic block models and topic models to improve entity-entity link modeling by jointly modeling links and text about the entities that are linked. We apply the model to two datasets: a protein-protein interaction (PPI) dataset supplemented with a corpus of abstracts of scientific publications annotated with the proteins in the PPI dataset and an Enron email corpus. The model is evaluated by inspecting induced topics to understand the nature of the data and by quantitative methods such as functional category prediction of proteins and perplexity which exhibit improvements when joint modeling is used over baselines that use only link or text information.

Naive Bayes classifier has long been used for text categorization tasks. Its sibling from the unsupervised world, the probabilistic mixture of multinomial models, has likewise been successfully applied to text clustering problems. Despite the strong independence assumptions that these models make, their attractiveness come from low computational cost, relatively low memory consumption, ability to handle heterogeneous features and multiple classes, and often competitiveness with the top of the line models. Recently, there has been several attempts to alleviate the problems of Naive Bayes by performing heuristic feature transformations, such as IDF, normalization by the length of the documents and taking the logarithms of the counts. We justify the use of these techniques and apply them to two problems: classification of products in Yahoo! Shopping and clustering the vectors of collocated terms in user queries to Yahoo! Search. The experimental evaluation allows us to draw conclusions about the promise that these transformations carry with regard to alleviating the strong assumptions of the multinomial model.