How A/B testing changes the dynamics of information spreading on a social network

TL;DR

This paper investigates how A/B testing influences information spread on social networks, revealing significant effects on content distribution and dynamics, using empirical data and agent-based modeling.

Contribution

It introduces an agent-based model to analyze A/B testing effects on social networks and explores potential policy interventions.

Findings

A/B testing significantly alters information dissemination dynamics.

Modeling shows potential for policy interventions like nudging and boosting.

Preliminary results highlight the impact on content distribution patterns.

Abstract

A/B testing methodology is generally performed by private companies to increase user engagement and satisfaction about online features. Their usage is far from being transparent and may undermine user autonomy (e.g. polarizing individual opinions, mis- and dis- information spreading). For our analysis we leverage a crucial case study dataset (i.e. Upworthy) where news headlines were allocated to users and reshuffled for optimizing clicks. Our centre of focus is to determine how and under which conditions A/B testing affects the distribution of content on the collective level, specifically on different social network structures. In order to achieve that, we set up an agent-based model reproducing social interaction and an individual decision-making model. Our preliminary results indicate that A/B testing has a substantial influence on the qualitative dynamics of information dissemination on a social network. Moreover, our modeling framework promisingly embeds conjecturing policy (e.g. nudging, boosting) interventions.