MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland.
Department of Psychology, University of Limerick, Limerick, Ireland.
Sci Rep. 2023 Mar 31;13(1):5249. doi: 10.1038/s41598-023-32295-z.
We consider the analysis of temporal data arising from online interactive social experiments, which is complicated by the fact that classical independence assumptions about the observations are not satisfied. Therefore, we propose an approach that compares the output of a fitted (linear) model from the observed interaction data to that generated by an assumed agent-based null model. This allows us to discover, for example, the extent to which the structure of social interactions differs from that of random interactions. Moreover, we provide network visualisations that identify the extent of ingroup favouritism and reciprocity as well as particular individuals whose behaviour differs markedly from the norm. We specifically consider experimental data collected via the novel Virtual Interaction APPLication (VIAPPL). We find that ingroup favouritism and reciprocity are present in social interactions observed on this platform, and that these behaviours strengthen over time. Note that, while our proposed methodology was developed with VIAPPL in mind, its potential usage extends to any type of social interaction data.
我们考虑了从在线互动社会实验中产生的时间序列数据的分析，由于观察到的观测数据不满足经典独立性假设，这使得分析变得复杂。因此，我们提出了一种方法，该方法将拟合（线性）模型的输出与基于假设的基于代理的零模型生成的输出进行比较。这使我们能够发现，例如，社交互动的结构与随机互动的结构有何不同。此外，我们还提供了网络可视化，用于识别群体内偏袒和互惠的程度，以及行为明显偏离规范的特定个体。我们特别考虑了通过新型虚拟交互应用程序（VIAPPL）收集的实验数据。我们发现，在这个平台上观察到的社交互动中存在群体内偏袒和互惠现象，而且这些行为随着时间的推移而增强。请注意，虽然我们提出的方法是针对 VIAPPL 开发的，但它的潜在用途可以扩展到任何类型的社交互动数据。
