Algorithmic Content Preference: Research Area — Prolific Citations Library

Discover 1 peer-reviewed study in Algorithmic Content Preference (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 1 peer-reviewed paper in the research area of Algorithmic Content Preference in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine

Authors: L Ma, J Qin, X Xu, Y Tan

Year: 2025

Published in: arXiv preprint arXiv:2509.14436, 2025•arxiv.org

Institution: University of North Carolina Charlotte, University of Science and Technology of China, University of Washington

Research Area: LLM behavior, Algorithmic content preference, Human–AI interaction

Discipline: Computer Science, Information Retrieval, Artificial Intelligence

This paper studies how generative search engines that use large language models (LLMs)—like Google’s AI overviews—select and cite web content, showing that these engines prefer content that is more predictable and semantically coherent for the model, and that LLM-based content polishing can increase the diversity and usefulness of AI summaries for users.

DOI: https://doi.org/10.48550/arXiv.2509.14436