Self-imposed Filter Bubble Model for Argumentative Dialogues

During their information seeking people tend to filter out all the parts of the available information that do not fit their existing beliefs or opinions. In this paper we present a model for this "Self-imposed Filter Bubble" (SFB) consisting of four dimensions. Thereby, we aim to 1) estimate the probability of the user being caught in an SFB and consequently, 2) identify suitable clues to reduce this probability in the further course of a dialogue. Using an exemplary implementation in an argumentative dialogue system, we demonstrate the validity and applicability of this model in an online user study with 102 participants. These findings serve as a basis for developing a system strategy to break the user's SFB and contribute to a sustainable and profound reflection on a topic from all viewpoints.