In the digital environment, algorithm is one of the main actors to undertake the information curation process. Algorithmic gatekeeping like recommender systems may determine to a large extent what information individuals see, which in turn may shape their beliefs. Among all the beliefs, political efficacy is widely recognized as an indicator of the health of representative democracy. It encompasses people’s confidence in their competency to participate in politics (internal efficacy), perception of system’s responsiveness (external efficacy), and beliefs in their community’s ability to reach a collective goal (collective efficacy). This research project explores the over-time dynamics between these efficacious feelings, political information, and algorithms.  

We will conduct this PhD project in three phrases:

1. Phase one: We will adopt a cross-platform approach to study recommender systems’ influence on political information perception and political efficacy among a representative Dutch population. 
2. Phase two: Employing digital trace data, our second phase focuses on examining the mechanisms behind the effects of recommender on specific dimensions of political efficacy.
3. Phase three: The objective of our third phase is to test how different design of recommender systems influence individuals’ exposure to recommender systems, and their efficacious feelings. 

This work is part of the larger NWO Gravitation project: Public Values in the Algorithmic Society (Algosoc).