Generative AI search broadens news topics and shared attention online
Breaking News Out of the Filter Bubble: Generative AI Search Diversifies Collective Attention and Raises Shared Information Consumption
Computers and SocietyHuman-Computer InteractionInformation RetrievalMultimedia
Summary
People worry that new AI tools for searching news might make readers see fewer topics and lose common ground. The authors studied over 37,000 readers using AI-powered search at The Washington Post. They found that AI answers led readers to a wider range of popular and less popular topics, increasing what they had in common without making news consumption narrower. Readers clicked differently, exploring AI-cited articles more and searching more often, which slightly raised the total article reading per person. Overall, generative AI helped make public attention more diverse but also more overlapping.
What this means in practice
- •For news platform developers: Design AI search features that increase topic diversity and shared information among readers to enhance engagement and public discourse.
- •For online content curators: Incorporate generative AI summaries with citations to guide users toward broader and less popular topics without reducing overall reading.
Authors
Heeseung Andrew Lee, Dokyun Lee, Gwanhoo Lee, Dongwon Lee
Abstract
Generative AI search and AI overviews are transforming access to information and news, renewing concerns that readers will encounter a narrower range of topics and have less in common. We examine these concerns via a randomized field experiment with 37,561 readers at The Washington Post. Both groups searched the same archive, but treatment readers also received AI answers with article citations above conventional results. Measuring consumption across displayed answers and opened articles, we find that AI search expands the reach of widely read topics and increases overlap in readers' topic consumption. At the same time, consumption becomes less concentrated and shifts toward less-popular topics, both within readers and across the audience. AI answers account for most of the increase in shared information, delivering it without requiring article clicks and broadening exposure beyond the articles readers open. Cited articles also contribute to the shift toward less-popular topics. Readers shift from conventional-result clicks and browsing toward cited articles and follow-up searches. More frequent searching offsets lower article consumption per search, producing a small increase in article consumption per reader. Total information consumption per minute also rises. Generative AI search can thus diversify collective attention while strengthening the information readers have in common.