Search engine results underpin many consequential decision making tasks. Examples include people using search technologies to seek health advice online, or time-pressured clinicians relying on search results to decide upon the best treatment/diagnosis/test for a patient.
A key problem when using search engines in order to complete such decision making tasks, is whether users are able to discern authoritative from unreliable information and correct from incorrect information. This problem is further exacerbated when the search occurs within uncontrolled data collections, such as the web, where information can be unreliable, generally misleading, too technical, and can lead to unfounded escalations (White&Horvitz, 2009). Information from search engine results can significantly influence decisions, and research shows that increasing the amount of incorrect information about a topic presented in a SERP can impel users to take incorrect decisions (Pogacar et al., 2017). As noted in the SWIRL III report (Culpepper et al., 2018), decision making with search engines is poorly understood, and likewise, evaluation measures for these search tasks need to be developed and improved.
This track aims to