Pitch Proposal: Recommenders with a Mission - Assessing Diversity in News Recommendations

By helping the user find relevant and important online content, news recommenders have the potential to fulfill a crucial role in a democratic society. Simultaneously, recent concerns about filter bubbles, fake news and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. This document details a pitch for an ongoing project that aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. Our aim is to get feedback on a set of proposed metrics grounded in social science interpretations of diversity.

Media Bias in German News Articles: A Combined Approach

Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. Models to identify and describe biases have been proposed across various scientific fields, focusing mostly on English media. In this paper, we propose a method for analyzing media bias in German media. We test different natural language processing techniques and combinations thereof. Specifically, we combine an IDF-based component, a specially created bias lexicon, and a linguistic lexicon. We also flexibly extend our lexica by the usage of word embeddings. We evaluate the system and methods in a survey (N = 46), comparing the bias words our system detected to human annotations. So far, the best component combination results in an F$$_{1}$$ 1 score of 0.31 of words that were identified as biased by our system and our study participants. The low performance shows that the analysis of media bias is still a difficult task, but using fewer resources, we achieved the same performance on the same task than recent research on English. We summarize the next steps in improving the resources and the overall results.

Monitoring Technoscientific Issues in the News

Research at the intersection between Science and Technology Studies (STS) and Public Communication of Science and Technology (PCST) investigates the role of science in society and how it is publicly perceived. An increasing attention has been paid to coverage of Science and Technology (S&T) issues in newspapers. Because of the availability of a huge amount of digitized news contents, the variety of the issues and their dynamic nature, new opportunities are offered to carry out STS and PCST investigations. The main contribution of this paper is a methodology and a system called TIPS that was co-shaped by sociologists and computer scientists in order to monitor the coverage of S&T issues in the news and to study how they are represented. The methodology relies on machine learning, information retrieval and data analytics approaches which aim at supporting expert users, e.g. sociologists, in the investigation of their research hypotheses.