arXiv subjects

Anna Lieb

Publications and source records attributed to Anna Lieb.

3 recordsLinked to original sources

arXiv2026

Investigating Click Behaviors On Google Search Result Pages That Produce an AI Overview

In 2024, Google introduced "AI Overviews," a feature that displays an AI-generated result summary at the top of many Google search pages. This study investigates the role of AI in Google search using one month of web browsing data from a representative panel of 900 U.S. adults. Our analysis of the panelists' Google searches sheds light on AI Overviews, when they appear in Google search results, and what user behaviors are associated with AI Overviews. We identify several attributes that make a search query more likely to generate an AI Overview, including the length of a query, whether the query begins with a question word, and whether the query contains both a noun and verb. When it comes to user behavior, we find that clicks to sources cited in AI Overviews are very rare, occurring in only about 1% of visits to AI Overviews. We also find that AI Overviews are associated with fewer clicks and higher rates of ending browsing sessions. Importantly, results from a mixed-effects logistic regression model indicate that these associations hold when controlling for random effects by panelist and query attributes that make AI Overviews more likely to appear.

cs.HC↗

arXiv2026

A large-scale corpus of religious radio broadcast transcripts from webstream recordings in the United States

Religious radio is a widespread but understudied form of mass communication in the United States, and content-level analysis of it has been constrained by the absence of large-scale transcript data. This Data Descriptor presents a corpus of transcribed English-language religious radio broadcasts captured from live webstreams over a one-month period in July 2025. Fifteen-minute segments were recorded on a rolling schedule from 785 distinct streams, which together rebroadcast the signals of more than two thousand AM and FM stations, yielding over 700,000 recordings and more than 60 million diarized lines of speech. Each recording was transcribed and speaker-diarized with an automated pipeline, and segmented and labeled by programming format and topic using a large language model. The corpus is organized as linked tables of stream metadata, recording metadata, and transcript lines. It supports descriptive study of religious broadcasting across regions and traditions, analysis of how social and political issues are discussed in religious media, and speech-processing research in an underrepresented domain.

cs.CL↗

arXiv2025

Creating Targeted, Interpretable Topic Models with LLM-Generated Text Augmentation

Unsupervised machine learning techniques, such as topic modeling and clustering, are often used to identify latent patterns in unstructured text data in fields such as political science and sociology. These methods overcome common concerns about reproducibility and costliness involved in the labor-intensive process of human qualitative analysis. However, two major limitations of topic models are their interpretability and their practicality for answering targeted, domain-specific social science research questions. In this work, we investigate opportunities for using LLM-generated text augmentation to improve the usefulness of topic modeling output. We use a political science case study to evaluate our results in a domain-specific application, and find that topic modeling using GPT-4 augmentations creates highly interpretable categories that can be used to investigate domain-specific research questions with minimal human guidance.

cs.CL↗