Trust and Trustworthiness in Social Recommender Systems
This addresses the issue of political polarization and partisan antipathy for users of social media, but it is incremental as it builds on existing trustworthiness concepts.
The paper tackles the problem of misinformation in social media by examining naive assumptions in ranking algorithms and proposes a user-centric trustworthiness framework to discourage dogmatization and build transparent news recommender systems.
The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions about network structure (like homophily) and group cognition (like, social action is largely imitative). Assumptions like these can be naïve and exclusionary in the era of fake news and ideological uniformity towards the political poles. We examine these assumptions with aid from the user-centric framework of trustworthiness in social recommendation. The constituent dimensions of trustworthiness (diversity, transparency, explainability, disruption) highlight new opportunities for discouraging dogmatization and building decision-aware, transparent news recommender systems.