Overview - Decision 2019

Proceedings | Data | Results | Runs | Participants

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. 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 Search Engine Result Page (SERP) can impel users to take incorrect decisions. As noted in the SWIRL III report, decision making with search engines is poorly understood, and likewise, evaluation measures for these search tasks need to be developed and improved. In this context, the TREC 2019 Decision track aims to (1) foster research on retrieval methods that promote better decision making with search engines, and (2) develop new online and offline evaluation methods to predict the decision making quality induced by search results.

Track coordinator(s):

• Mustafa Abualsaud, University of Waterloo
• Mark D. Smucker, University of Waterloo
• Christina Lioma, University of Copenhagen
• Maria Maistro, University of Copenhagen
• Guido Zuccon, University of Queensland

Track Web Page: https://trec-health-misinfo.github.io/2019.html

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