Data Voids and Warning Banners on Google Search
Ronald E. Robertson, Evan M. Williams, Kathleen M. Carley, David Thiel

TL;DR
This study investigates Google's warning banners for data voids in search results, analyzing their occurrence, characteristics, and the underlying data voids, revealing many more unflagged issues and highlighting transparency concerns.
Contribution
The paper introduces deep learning models to detect data voids beyond Google's warnings and provides a comprehensive analysis of warning banner usage and its limitations.
Findings
Google issued warning banners for about 1% of queries.
Low-quality banners are rare and linked to conspiracy keywords.
Many more data voids exist than banners indicate.
Abstract
The content moderation systems used by social media sites are a topic of widespread interest and research, but less is known about the use of similar systems by web search engines. For example, Google Search attempts to help its users navigate three distinct types of data voids--when the available search results are deemed low-quality, low-relevance, or rapidly-changing--by placing one of three corresponding warning banners at the top of the search page. Here we collected 1.4M unique search queries shared on social media to surface Google's warning banners, examine when and why those banners were applied, and train deep learning models to identify data voids beyond Google's classifications. Across three data collection waves (Oct 2023, Mar 2024, Sept 2024), we found that Google returned a warning banner for about 1% of our search queries, with substantial churn in the set of queries…
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Taxonomy
TopicsData-Driven Disease Surveillance
MethodsSparse Evolutionary Training
