The Impact of Snippet Reliability on Misinformation in Online Health Search
CoRR(2024)
Abstract
Search result snippets are crucial in modern search engines, providing users
with a quick overview of a website's content. Snippets help users determine the
relevance of a document to their information needs, and in certain scenarios
even enable them to satisfy those needs without visiting web documents. Hence,
it is crucial for snippets to reliably represent the content of their
corresponding documents. While this may be a straightforward requirement for
some queries, it can become challenging in the complex domain of healthcare,
and can lead to misinformation. This paper aims to examine snippets'
reliability in representing their corresponding documents, specifically in the
health domain. To achieve this, we conduct a series of user studies using
Google's search results, where participants are asked to infer viewpoints of
search results pertaining to queries about the effectiveness of a medical
intervention for a medical condition, based solely on their titles and
snippets. Our findings reveal that a considerable portion of Google's snippets
(28
that 35
compared to their corresponding documents. To address this issue, we propose a
snippet extraction solution tailored directly to users' information needs,
i.e., extracting snippets that summarize documents' viewpoints regarding the
intervention and condition that appear in the query. User study demonstrates
that our information need-focused solution outperforms the mainstream
query-based approach. With only 19.67
reported as not presenting a viewpoint and a mere 20.33
participants. These results strongly suggest that an information need-focused
approach can significantly improve the reliability of extracted snippets in
online health search.
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