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.

翻译：A/B测试方法通常由私营公司用于提高用户对在线功能的参与度和满意度。其使用远非透明，且可能损害用户自主权（例如极化个人观点、传播错误和虚假信息）。在我们的分析中，我们利用了一个关键案例研究数据集（即Upworthy），其中新闻标题被分配给用户并重新排列以优化点击量。我们的研究重点是确定A/B测试如何以及在何种条件下影响集体层面的内容分布，特别是在不同的社交网络结构上。为此，我们建立了一个基于主体的模型来再现社交互动，并建立了个体决策模型。初步结果表明，A/B测试对社交网络上信息传播的定性动态具有显著影响。此外，我们的建模框架有望嵌入推测性政策（例如助推、强化）干预措施。