Selection Bias in News Coverage: Learning it, Fighting it

News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations

With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical recommendation system, that utilizes social annotations, to help Twitter users discover tweets, on topics of their interest. A major challenge in using traditional rating dependent recommendation systems, like collaborative filtering and content based systems, in high volume social networks is that, due to attention scarcity most items do not get any ratings. Additionally, the fact that most Twitter users are passive consumers, with 44% users never tweeting, makes it very difficult to use user ratings for generating recommendations. Further, a key challenge in developing recommendation systems is that in many cases users reject relevant recommendations if they are totally unfamiliar with the recommended item. Providing a suitable explanation, for why the item is recommended, significantly improves the acceptability of recommendation. By virtue of being a topical recommendation system our method is able to present simple topical explanations for the generated recommendations. Comparisons with state-of-the-art matrix factorization based collaborative filtering, content based and social recommendations demonstrate the efficacy of the proposed approach

PICAE – Intelligent publication of audiovisual and editorial contents

The development in internet infrastructure and technology in last tow decades have given users and retailers the possibility to purchase and sell items online. This has of course broadened the horizons of what products can be offered outside of the traditional trading sense, to the point where virtually any product can be offered. These massive online markets have had a considerable impact on the habits of consumers, providing them access to a greater variety of products and information on these goods. This variety has made online commerce into a multi-billion dollar industry but it has also put the customer in a position where it is getting increasingly difficult to select the products that best fit their individual needs. In the same vein, the rise of both availability and the amounts of data that computers have been able to process in the last decades have allowed for many solutions that are computationally expensive to exist, and recommender systems are no exception. These systems are the perfect tools to overcome the information overload problem since they provide automated and personalized suggestions to consumers. The PICAE project tackles the recommendation problem in the audiovisual sector. The vast amount of audiovisual content that is available nowadays to the user can be overwhelming, which is why recommenders have been increasingly growing in popularity in this sector ---Netflix being the biggest example. PICAE seeks to provide insightful and personalized recommendations to users in a public TV setting. The PICAE project develops new models and analytical tools for recommending audiovisual and editorial content with the aim of improving the user experience, based on their profile and environment, and the level of satisfaction and loyalty. These new tools represent a qualitative improvement in the state of the art of television and editorial content recommendation. On the other hand, the project also improves the digital consumption index of these contents based on the identification of products that these new forms of consumption demand and how they must be produced, distributed and promoted to respond to the needs of this emerging market. The main challenge of the PICAE project is to resolve two differentiating aspects with respect to other existing solutions such as: variety and dynamic contents that requires a real-time analysis of the recommendation and the lack of available information about the user, who in these areas is reluctant to register, making it difficult to identify in multi-device consumption. This document will explain the contributions made in the development of the project, which can be divided in two: the development of the project, which can be divided in two: the development of a recommender system that takes into account information of both users and items and a deep analysis of the current metrics used to assess the performance of a recommender system