The Impact of AI Search on the Online Content Ecosystem: Evidence from Google and Reddit
- URL: http://arxiv.org/abs/2605.16428v2
- Date: Wed, 20 May 2026 04:45:28 GMT
- Title: The Impact of AI Search on the Online Content Ecosystem: Evidence from Google and Reddit
- Abstract summary: generative AI search tools that summarize answers directly on the results page may disrupt this relationship by making visits to source platforms optional.<n>We study this question using Google AI Overviews and Reddit, one of the largest online discussion platforms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence of generative AI search tools that summarize answers directly on the results page may disrupt this relationship by making visits to source platforms optional. We study this question using Google AI Overviews and Reddit, one of the largest online discussion platforms. Our identification exploits Google's content moderation policy: Safe-for-Work (SFW) Reddit communities are indexed by Google organic search and surfaced in Google AI Overviews, while Not-Safe-for-Work (NSFW) communities, though indexed by organic search, are prohibited from being referenced in AI Overview summaries. Using a difference-in-differences design, we find that AI Overviews increase engagement in SFW communities: daily comments rise by 12.0 percent and the number of commenting users by 12.3 percent relative to NSFW communities. The effects are concentrated in experience-based discussions (opinions, advice, and personal experiences) rather than fact-based information. However, the subsequent introduction of Google AI Mode, which allows users to interact conversationally with the AI summary, largely eliminates these gains in experience-based content. These results suggest that the effects of AI search depend critically on interface design and types of content.
Related papers
- AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence [2.303636351420161]
We estimate the causal effects of AI Overviews and AI Mode on user behavior, perceptions, and publisher traffic on Google Search.<n>We show that removing AI Overviews and AI Mode increases click-through rates to publishers, while an AI Mode-only experience reduces click-through rates and erodes user experience and trust in information found on Google.
arXiv Detail & Related papers (2026-08-18T22:08:10Z) - How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews [5.313243639890497]
We compare the search results returned by Google's search engine, the accompanying AI Overview (AIO), and Gemini Flash 2.5 for each query.<n>For 51.5% of representative, real-user queries, AIOs are generated, and are displayed above the organic search results.<n>Traditional Google search is significantly more likely to retrieve information from popular or institutional websites in government or education.<n>Generative search engines are significantly more likely to retrieve Google-owned content.
arXiv Detail & Related papers (2026-04-30T12:32:21Z) - A debate game about societal impacts of Artificial Intelligence [0.0]
We propose a debate game simulating a municipal council gathered to choose between three AI solutions proposed by a company to address a societal problem.<n>This interactive tool is available for free download online.
arXiv Detail & Related papers (2026-03-04T09:44:15Z) - AI summaries in online search influence users' attitudes [3.459756369056329]
This study examined how AI-generated summaries affect how users think about different issues.<n>Users perceived the AI summaries more useful when it emphasized health harms versus benefits.<n>These findings suggest that AI-generated search summaries can significantly shape public perceptions.
arXiv Detail & Related papers (2025-11-27T23:45:19Z) - CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
arXiv Detail & Related papers (2025-08-28T06:03:11Z) - Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits [17.65917938185025]
generative AI models like Dall-E have made it possible for anyone to create compelling visual art.<n>In online communities, the introduction of AI-generated content (AIGC) may impact social dynamics.<n>We take steps towards examining the potential impact of AIGC on art-related communities on Reddit.
arXiv Detail & Related papers (2024-10-09T17:41:13Z) - Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated [48.70176791365903]
This study explores how bias shapes the perception of AI versus human generated content.<n>We investigated how human raters respond to labeled and unlabeled content.
arXiv Detail & Related papers (2024-09-29T04:31:45Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - What Do End-Users Really Want? Investigation of Human-Centered XAI for
Mobile Health Apps [69.53730499849023]
We present a user-centered persona concept to evaluate explainable AI (XAI)
Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences.
Our insights bring an interactive, human-centered XAI closer to practical application.
arXiv Detail & Related papers (2022-10-07T12:51:27Z) - The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations.
We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
arXiv Detail & Related papers (2021-07-28T17:32:04Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.