Algorithmic bias and multidimensional political polarisation in online social networks

Presentation Slides (PDF)

Abstract

Political polarization in online social networks is a rapidly growing phenomenon worldwide, happening within a pervasive, variable, and mostly hidden ecosystem of content filtering technologies that determine what information we see online. Despite the relevance of algorithms and opinion formation mechanisms to modern-day political discourse, the structure and dynamics of polarized states in digital spaces are still poorly understood.

In this talk I’ll introduce a general theoretical framework to systematically link models of opinion dynamics, social network structure, and content filtering, allowing us to pinpoint what type of social interactions are robust against algorithmic bias, and which ones are susceptible to bias-enhanced opinion polarization. I’ll also introduce a framework of ideological embedding and opinion dynamics modelling that uncovers multidimensional political polarization in Twitter.

Together, our results shed light on the social behaviors that drive digital platforms towards polarization, and give theoretical ground for the development of heuristics to tackle harmful effects of online bias, such as information bottlenecks, echo chambers, and opinion radicalization.

Relevant publications

Effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions

Multidimensional political polarization in online social networks

Opinion dynamics in social networks: From models to data

Opinion formation on social networks with algorithmic bias: dynamics and bias imbalance

Inferring attitudinal spaces in social networks