Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content (2021)

Abstract: In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate. To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.

## introduction recommender systems are very present in our online experience. services recommend movies in movie streaming platforms, possible purchases in online shops, and news articles on news sites. people increasingly live their lives through digital environments that recommend some items over others. recommender systems thus have a significant influence on the lived experiences of people. precisely because we can expect recommender systems to perform ever more (important) functions in the near future, it is essential to incorporate the people they end up influencing in our thinking about recommender systems. we focus on one case in this paper: news recommender systems (nrs) such as news aggregators, online newspapers, or news widgets. we believe natural language processing (nlp) should have a more prominent role in the development of nrs. these systems are an interesting case because the role of the media has historically always been understood as essential to democratic societies and democratic debate @xcite . our central argument is that the users of nrs are not just collections of data points, but are democratic citizens, who perform more social roles than that of a consumer. we propose a research agenda to put the user as a human being at the centre of nlp and computer science research into recommender systems. for a functioning democracy, we want users to come into contact with opinions, debates, or ideas they disagree with or even dislike. this implies that simply optimizing a news recommender system for user preference, as is common now, is not enough. the public value of diversity, in terms of a diversity of issues and opinions, is essential @xcite . there already is work on the question of how opinion, sentiment, and argument diversity for news recommendation should be understood and captured by (evaluation) metrics @xcite , or nlp tasks @xcite . in this paper, we want to develop a different additional perspective. we propose that a turn to the user -i.e., democratic citizens reading news recommended by nrs -is needed. this paper explores challenges and opportunities for facilitating this with the help of nlp. more specifically, we propose the notion of individual latitutes of diversity which can help make the diversity of news recommendations more meaningful by taking the user-as-a-human-being into account. although the promotion of news diversity is desirable from societal perspective, not every user has a similar acceptance of diversity. simply maximizing the diversity of news recommendations can therefore have serious backfire effects @xcite . we combine perspectives from communication science, nlp, and ethics to develop this contribution. building on theories from communication science, we explore how the notion of latitude of diversity can help facilitate engagement with diverse content. work in the nlp field is used to suggest how nrs can capture viewpoint diversity in news articles and connect to individual users' latitudes of diversity. lastly, we incorporate ethical reflections on the value and need of diversity, as well as on some of the risks of designing (dataintensive) personalized recommendation engines. in our paper, these different fields and perspectives all contribute to answer one central question: how do we nudge citizens towards actual engagement with diverse viewpoints in news recommender systems with nlp techniques, while treating the user as a complex human and democratic citizen? to answer this question, our paper is organized as follows. section 2 introduces why viewpoint diversity in the news context is important for democracy, how nlp is connected, and why focusing on maximizing viewpoint diverse recommendations is not enough. section 3 presents our ideas and discusses the current technical and conceptual challenges relevant for building a more user-centric news recommender. section 4 explores some important ethical questions that should be considered before implementing our ideas. lastly, we restate our argument in section 5. ## background we first provide some background literature for our idea for diverse news recommendation. section 2.1 addresses why viewpoint news diversity is important from a democratic perspective. in section 2.2, we introduce how viewpoint diversity is connected to nlp. we then discuss nudging theory and how it can inform news recommender design in section 2.3. lastly, we explain how our concept of "latitude of diversity" can help make news recommenders more user-centric in section 2.4. ## ideas and challenges for user-centric diverse recommendation earlier solutions to diversity in nrs have focused on author or metadata-based diversity @xcite , or on optimizing for diversity without considering user interest and susceptibility @xcite . we take the next step towards support of democratic values in nrs: putting the user central, and especially the individual's latitude of diversity. we aim to optimize not for user preference, but for the user's diversity range required to participate in a functioning democracy. this is a more nuanced and long-term goal than the shortterm one of user preference. in order to answer our research question, we will discuss the different elements in this problem separately in the following subsections. ## conclusion in this paper, we presented an important objective for societal impact of nlp: (viewpoint) diversity in news recommendation to support a healthy democratic debate. going further than previous work, we connect diversity in news recommendation to democratic theory and to findings in communication science on individual user differences in acceptance of diversity. we conclude that to foster a healthy democratic public debate, we should detect viewpoints, but also detect individual latitudes of diversity. nlp can play a pivotal role in these tasks: vector space models would allow us to place different users (or user representations) and news articles in a multidimensional space, where diversity is operationalized as distance and variance. thereby, we could personalize different users' latitudes of diversity, and accordingly deliver diverse recommendations that support a healthy public debate while still keeping the user satisfied. however, we also point out several technical, conceptual, and ethical problems that show this objective needs more than the "plug and play" of nlp solutions, but rather requires further research and careful reflection.