Data Portraits and Intermediary Topics

Abstract

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.

Quantifying Biases in Online Information Exposure

“…More generally, we are somewhat more likely to be exposed to news articles aligned with our worldviews when we browse online (Flaxman, Goel, & Rao, ). These effects, however, are not static—the way in which news are presented can affect the willingness of users to engage with a piece of information with which they disagree (Doris‐Down, Versee, & Gilbert, ; Graells‐Garrido, Lalmas, & Baeza‐Yates, ; Graells‐Garrido, Lalmas, & Quercia, ; Munson & Resnick, ). Exposing the choices of others may also lead to homogeneity bias (Salganik, Dodds, & Watts, ).…”

Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models

“…The surface realization task bears the closest resemblance to the SemEval 2017 shared task AMR-to-text (May and Priyadarshi, 2017). Our approach to data augmentation and preprocessing uses many insights from Neural AMR (Konstas et al, 2017). Traditional datato-text systems use a rule based approach (Reiter and Dale, 2000).…”

“…Therefore, we construct the original parse tree from the dependency features, and perform a depth-first search to sort and reorder the lemmas. This is similar to the linearization step performed by Konstas et al (2017), the main difference being we randomly choose between child nodes instead of using a predetermined order based on edge types.…”

“…The key idea is to create a separate embedding representation for each feature type, and to concatenate the embeddings for each input token to create its dense representation. Sennrich et al (2016) showed that this technique is quite effective for neural machine translation, and some recent work, such as Hokamp (2017) factors F is created as in Eq. 1:…”

End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights

“…Attention-based neural networks are increasingly popular because of their ability to focus on the most important segments of a given input. These models have proven to be extremely effective in many different tasks, for example neural machine translation (Luong et al, 2015;Tu et al, 2016), neural image caption generation (Xu et al, 2015), and multiple sub-tasks in question answering (Hermann et al, 2015;Yin et al, 2016;Andreas et al, 2016).…”