With Maps and Mobs: Searching for Trustworthiness using Belief Spaces

Abstract

The detection of echo chambers and information bubbles is becoming increasingly relevant in this era of polarized information. It may be possible to evaluate information trustworthiness by examining the behavior of individuals in belief space rather than evaluating the information itself, which is a harder problem. To explore this, I propose to research a model for information retrieval that integrates two levels of information interaction. On the individual level, I leverage Munson and Resnick»s diversity-seeker, confirmer, and avoider patterns. At a group level, I integrate individual behaviors according to Moskivici»s work on crowd polarization. These perspectives have been integrated in a simulation that employs insights from animal collective behavior to model agent groups, which enable the systematic exploration of belief navigation behaviors that can be detected algorithmically. Viewing information retrieval from the perspective of belief spaces may shed light on current practices and lay out consideration for future design work.