Civic Ground Truth in News Recommenders: A Method for Public Value Scoring

TL;DR

This paper presents a new method for integrating civic values into news recommendation systems by using large-scale audience evaluations and automated metadata enrichment to generate generalisable civic ground truth labels.

Contribution

It introduces a structured approach for embedding civic values into NRS through nationally representative surveys and automated metadata, advancing civic-minded news recommendation.

Findings

Generated civic ground truth labels from survey data

Demonstrated the generalisability of civic value labels across news corpus

Enhanced news recommendation systems with normative goal integration

Abstract

Research in news recommendation systems (NRS) continues to explore the best ways to integrate normative goals such as editorial objectives and public service values into existing systems. Prior efforts have incorporated expert input or audience feedback to quantify these values, laying the groundwork for more civic-minded recommender systems. This paper contributes to that trajectory, introducing a method for embedding civic values into NRS through large-scale, structured audience evaluations. The proposed civic ground truth approach aims to generate value-based labels through a nationally representative survey that are generalisable across a wider news corpus, using automated metadata enrichment.