Incentivizing Citizen Exposure to Quality News Online: Framework and Tools

Introduction

Populism, polarisation, and wavering support for democratic norms are pressing threats to the EU. NEWSUSE seeks to explain and remedy these threats by bringing the problem of insufficient news consumption to the forefront. Rather than focusing on echo chambers, misinformation, or filter bubbles, I argue that very low news use among citizens is a drastically overlooked problem, which leads to voting misaligned with individual interests and susceptibility to manipulative, populist, and misinformed rhetoric. Brexit and the 2016 US election are key examples.

Importance of News Consumption

Because greater news exposure can make citizens more resilient to the various democratic threats, it is urgent to address the fundamental question: How to sustainably incentivize the use of quality news?

Theoretical Framework

Toward this end, NEWSUSE advances a theoretically driven and applicable model that accounts for individual, algorithmic, and social factors influencing news (non-)use and – therefore – also the approaches to promoting news intake.

Computational Tools

Building on this model, NEWSUSE designs validated computational tools to:

1. Increase people’s consumption of quality news.
2. Pinpoint whether it is the citizens, social contexts, and/or recommender systems that need to be tweaked.
3. Test the democratic effects of increased news use across political contexts and social media platforms.

Methodology

NEWSUSE relies on cutting-edge multidisciplinary methods, combining:

• Quantitative and qualitative approaches.
• Observational and over-time experimental designs.
• Incorporating the latest advances in computer and computational social science.

It is the first project to apply these innovative approaches conjointly and comparatively.

Impact and Knowledge Advancement

NEWSUSE advances academic knowledge, and its findings will be crucial for scholars across disciplines, (non-)governmental organizations, and policymakers working on algorithmic transparency. It is only when we know how different factors together prevent or enhance news exposure that we will be able to make citizens and democracies more resilient.