Understanding user-platform interactions and algorithmic impact: The development and implementation of a novel research methodology on Google Search

We are constantly confronted with an abundance of online information. One way to deal with this is to rely on algorithms to select and prioritize information. Despite the aura of objectivity, such algorithms are not value-free: they are sensitive to biases and external influences. Algorithms are often suspected of distorting information filtering, which in turn is assumed to affect our beliefs. This is an assumption that ignores the reality that algorithmic outcomes only exist in a continuous interaction between users and the online platform. Users can resist algorithms and even influence them. The complex reciprocal relationship between users and algorithms is too often lost in empirical research. This project aims to investigate the role of users in two forms of algorithm bias in the context of Google Search: (a) general bias of search results and (b) individualized bias in the form of sponsored results, and that for two cases : (a) political information and (b) health information. To accomplish this, a new method is developed based on an in-house developed customized online platform that allows to emulate differences in the Google Search algorithms and to investigate its impact on user behaviour and cognitions.