Using eye-tracking, we investigate how searchers interact with Web search engines which get affected by nonsensical results. We conduct a user survey to choose "stupid" components for our laboratory experiment and explore the most conspicuous ones.

One of the main targets of any search engine is to make every user fully satisfied with her search results. For this reason, lots of efforts are being paid to improving ranking models in order to show the best results to users. However, there is a class of documents on the Web, which can spoil all efforts being shown to the users. When users receive results, which are not only irrelevant, but also completely out of the picture of their expectations, they can get really frustrated. So, we attempted to find a method to determine such documents and reduce their negative impact upon users and, as a consequence, on search engines in general.