Impact of Biased Search Results on User Engagement in Web Search
W.R.A. Turk (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Ujwal Gadiraju – Mentor (TU Delft - Web Information Systems)
Geert-Jan Houben – Mentor (TU Delft - Web Information Systems)
Y. Chen – Graduation committee member (TU Delft - Data-Intensive Systems)
David Maxwell – Coach (TU Delft - Web Information Systems)
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Abstract
Search engines play an important role in the provision of information. Recently, researchers have raised their concerns about the potential of search engine providers trying to shift the opinion of their users by manipulating search results. But search results might also be manipulated for financial gain. If an increase in User Engagement (UE) can be achieved by manipulating search results, the manipulation of search results can lead to increased advertisement revenue. In this thesis, a 2 x 3 x 3 factorial user study (n = 376) investigating the effect of biased Search Engine Results Pages (SERPs) on UE in users with strong opinions on the topic is presented. Different search result ranking biases were used: a search result ranking biased to agree with the topic, one that disagrees with the topic and one that is indifferent towards the topic. One of two different topics is assigned to the user. The bias in the SERP is determined by the condition assigned to the user (i.e., mitigate, control or reinforce) and the viewpoint of the user towards the topic (i.e., agree or disagree). Furthermore, one out of three queries is presented at the top of the SERP (i.e., a query that agrees, disagrees or is impartial with the topic). It was found that neither mitigating nor reinforcing bias through the SERP affected UE. The exploratory analysis suggests that confirmation bias did not guide the results clicked by the users. Users were not refrained from clicking search results that do not align with their viewpoint on the topic. Instead, the exploratory analysis suggests that results are clicked based on their position (position bias), regardless of whether bias of the user is mitigated or reinforced.