  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.
