The Impact of Snippet Reliability on Misinformation in Online Health Search

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%) failed to present any viewpoint on the intervention's effectiveness, and that 35% were interpreted by participants as having a different viewpoint 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% of snippets generated by our solution reported as not presenting a viewpoint and a mere 20.33% misinterpreted by participants. These results strongly suggest that an information need-focused approach can significantly improve the reliability of extracted snippets in online health search.

Not Just Skipping. Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health Search

Advertisements (ads) are an innate part of search engine business models. Advertisers are willing to pay search engines to promote their content to a prominent position in the search result page (SERP). This raises concerns about the search engine manipulation effect (SEME): the opinions of users can be influenced by the way search results are presented. In this work, we investigate the connection between SEME and sponsored content in the health domain. We conduct a series of user studies in which participants need to evaluate the effectiveness of different non-prescription natural remedies for various medical conditions. We present participants SERPs with different intentionally created biases towards certain viewpoints, with or without sponsored content, and ask them to evaluate the effectiveness of the treatment only based on the information presented to them. We investigate two types of sponsored content: 1. Direct marketing ads that directly market the product without expressing an opinion about its effectiveness, and 2. Indirect marketing ads that explicitly advocate the product's effectiveness on the condition in the query. Our results reveal a significant difference between the influence on users from these two ad types. Though direct marketing ads are mostly skipped by users, they can tilt users decision making towards more positive viewpoints. Indirect marketing ads affect both the users' examination behaviour and their perception of the treatment's effectiveness. We further discover that the contrast between the indirect marketing ads and the viewpoint presented in the organic search results plays an important role in users' decision-making. When the contrast is high, users exhibit a strong preference towards a negative viewpoint, and when the contrast is low or none, users exhibit preference towards a more positive viewpoint.