Using Agent-Based Modelling to Evaluate the Impact of Algorithmic Curation on Social Media

Abstract

Social media networks have drastically changed how people communicate and seek information. Due to the scale of information on these platforms, newsfeed curation algorithms have been developed to sort through this information and curate what users see. However, these algorithms are opaque and it is difficult to understand their impact on human communication flows. Some papers have criticised newsfeed curation algorithms that, while promoting user engagement, heighten online polarisation, misinformation, and the formation of echo chambers. Agent-based modelling offers the opportunity to simulate the complex interactions between these algorithms, what users see, and the propagation of information on social media. This article uses agent-based modelling to compare the impact of four different newsfeed curation algorithms on the spread of misinformation and polarisation. This research has the following contributions: (1) implementing newsfeed curation algorithm logic on an agent-based model; (2) comparing the impact of different curation algorithm objectives on misinformation and polarisation; and (3) calibration and empirical validation using real Twitter data. This research provides useful insights into the impact of curation algorithms on how information propagates and on content diversity on social media. Moreover, we show how agent-based modelling can reveal specific properties of curation algorithms, which can be used in improving such algorithms.