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October 25, 2018
Title: The closed loop between opinion formation and personalised recommendations
Abstract:
The literature on social dynamics contains many examples of mathematical models of opinion for- mation, that capture the influence between individuals and the effects of partisan media. Nowadays, a large part of social interactions and information gathering happens online, where it is mediated by recommender systems that guide the users to relevant content. An excessive personalisation might however artificially distort user’s perception and exacerbate opinion polarisation: while experimental research has investigated the issue, no mathematical model features the active role played by recommender systems. In this work, we aim to close this gap by making explicit the feedback loop between opinion formation and the recommendation of personalised contents. We focus on a single user interacting with an idealised online news aggregator, with the uses having the tendency to prefer confirmatory news. We define metrics for the opinion polarisation and news aggregator efficiency, and perform both extensive numerical simulations and a mathematical analysis. We find that personalised contents and confirmation bias contribute to opinion polarisation.
