1 research outputs found
Trust and Trustworthiness in Social 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 na\"ive 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.Comment: WWW '19 FATE