No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems (2021)

Abstract: Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.

## introduction the field of natural language processing (nlp) uses specific, self-defined definitions for separate tasks -each with their own leaderboards, benchmark datasets, and performance metrics. when dealing with complex, societal problems, it may however be better to take into account a broader view, starting from the actual needs to solve the overall societal problem. in particular, this paper addresses the complex issue of non-diverse news recommenders potentially threatening democracy @xcite . we focus on a theory of democracy and its role in news recommendation, as described in @xcite , and reflect on which nlp tasks may help address this issue. in doing so, we consider work by experts on the problem and domain, such as political scientists, recommender system experts, philosophers and media and communication experts. news recommender systems play an increasingly important role in online news consumption @xcite . such systems recommend several news articles from a large pool of possible articles whenever the user wishes to read news. recommender systems usually attempt to make the recommended articles increase the user's interaction and engagement. in a news recommender system, this typically means optimizing for the individual user's "clicks" or "reading time" @xcite . these measures are considered a proxy for reader interest and engagement, but other metrics could also be used, including the time spent on a page or article ratings. recommender systems are tailored to individual user interests. for other types of recommender systems, e.g. entertainment systems (recommending music or movies), this is less of a problem. however, news recommendation is connected to society and democracy, because news plays an important role in keeping citizens informed on recent societal issues and debates @xcite . personalization to user interest in the news recommendation domain can lead to a situation where users are increasingly unaware of different ideas or perspectives on current issues. the dangers of such news 'filter bubbles' @xcite and online 'echo chambers' @xcite due to online (over)personalization have been pointed out before @xcite . political theory provides several models of democracy, which each also imply different roles for news recommendation. we follow the deliberative model of democracy, which states citizens of a functioning democracy need to get access to different ideas and viewpoints, and engage with these and with each other @xcite (a further explanation of this model is given in section 2). a uniform news diet and personalization to only personal interests can, in theory if not in practice, lead to a narrow view on current issues and a lack of deliberation in democracy. when considering this model, it becomes clear that news personalization on user interest alone is potentially harmful for democracy. the normative goal of a recommender system then becomes: supporting a deliberative democracy by showing a diverse set of views to users. nlp can play a role here, by automatically identifying viewpoints, arguments, or claims in news texts. output of such trained models can help recommend articles that show a diverse set of views and arguments, and thus support a deliberative democracy. the explicit goals and underlying values of democracy expressed in the model of deliberative democracy can help in defining what nlp tasks and analyses are relevant for tackling the potential harmful effects of news recommendation. this can increase the societal impact of relevant nlp tasks. we believe considering such theories and normative models can also help work on other complex concepts and societal problems where nlp plays a role. in this paper, we outline societal challenges and a theoretical model of the role of non-diverse news recommenders in democracy, as developed by experts such as political scientists and media experts. we then argue that argument mining, viewpoint detection, and related nlp tasks can make a valuable contribution to the effort in diversifying news recommendation and thereby supporting a deliberative democracy. this position paper provides the following contributions to the discussion: we argue that taking normative and/or societal goals into account can provide insights in the usefulness of specific nlp tasks for complex societal problems. as such, we believe that approaching such problems from an interdisciplinary point of view can help define nlp tasks better and/or increase their impact. in particular, we outline the normative and societal goals for diversifying news recommendation systems and illustrate how these goals relate to various nlp tasks. this results in a discussion on how, on the one hand, news recommendation can make better use of nlp and, on the other hand, how the goal of diversifying news provides inspiration for improving existing tasks or developing new ones. this paper is structured as follows: we first describe the problem that personalized news recommendation could pose for democracy, as well as the importance of an interdisciplinary approach to solving this problem in section 2. section 3 provides an overview of literature tackling diversity in news recommendation as a solution to this problem, and points out remaining gaps in these efforts, specifically connected to the idea of a deliberative democracy. section 4 outlines several related nlp tasks and their connection to this overarching normative goal. in section 5, we discuss what we think the nlp community should take away from this reflection, and in section 6 we will conclude our paper. ## personalization in the news, theories of democracy, and interdisciplinarity the online news domain has increasingly moved towards personalization @xcite . in the news domain, such personalization comes with specific issues and challenges. a combination of personalizaton and (political) news can lead to polarization, filter bubbles @xcite , and echo chambers @xcite . this trend to personalize leads to shared internet spaces becoming much more tailored to the individual user rather than being a shared, public space @xcite . such phenomena could negatively impact a citizen's rights to information and right to not be discriminated @xcite . evidence for filter bubbles is under discussion @xcite , but empirical work does indicate that especially fringe groups holding extreme political or ideological opinions may end up into such a conceptual bubble @xcite . @xcite points out that a lack of diversity in news recommendation can also harm democracy. this clearly holds for the deliberative model of democracy. this model assumes that democracy functions on deliberation, and the exchange of points of view. a fundamental assumption in this model is that individuals need access to diverse and conflicting viewpoints and argumentation to participate in these discussions @xcite . news recommendations supporting a deliberative democracy should then play a role in providing access to these different viewpoints, ideas, and issues in the news @xcite . the threat to democracy of non-diverse news recommenders is a complex problem. it requires input from different academic disciplines, from media studies and computer science to political science and philosophy @xcite . political theory can provide a framework that helps define what is needed from more empirical and technical researchers to address this problem. in the next section, we will discuss recent work in diversity in news recommendation. we point out remaining gaps in these efforts, specifically connected to the idea of a deliberative democracy. ## discussion in the previous section, we have outlined a number of relevant nlp tasks, and made their possible contribution to the support of a deliberative democracy through diverse news recommendation explicit. in the following section, we discuss the implications and considerations following from these separate tasks for diversity in news recommendations, and provide some advice for nlp researchers. ## conclusion in this paper, we have provided an overview of several separate nlp tasks related to news recommender system diversity, especially considering the normative goal of a deliberative democracy. an explicit incorporation of such over-arching normative goals is currently missing in these tasks, while this is conceptually very useful and societally relevant. as such, taking this end goal into account can help improve social relevance of nlp and support nlp researchers in defining specific goals and next steps in their research. research on recommendation systems could benefit from more specific work that operationalizes the theoretical concepts in democratic theory. such operationalizations should start with the groundwork laid by nlp tasks such as stance detection, argumentation mining and tasks aiming at detecting frames, propaganda and polarization. however, current nlp tasks do not address problems related to viewpoint diversity in news recommendation in its full complexity yet. nlp should take the complexities of news and the news recommendation domain into account. news texts often contain more than one stance or argument, and they tend to have more implicitly expressed viewpoints than other texts. moreover, news comes with the challenge that new topics constantly appear and training data on detecting viewpoints in some issues may not generalize well to new data on other topics or issues. this leads us to the following two concrete steps for future work, specifically in nlp: (1) researchers should further advance methods that aim to identify more subtle ways in which viewpoints occur in real-world news text; (2) methods should address the issue of constant changes in data, with one possible solution being one-shot learning. last but not least, in order to find out how these tasks can truly be used to improve a deliberative democracy, we face the challenge of evaluating beyond assigning correct labels to pieces of text. this brings us back to the main message of this paper: answering this question goes beyond the expertise of nlp researchers. in order to maximize the impact of our technologies for addressing this complex problem, we need expertise from other disciplines.