# A modelling methodology for social interaction experiments

## TL;DR

This paper introduces a new modeling methodology for analyzing temporal social interaction data from online experiments, addressing limitations of standard statistical methods and enabling insights into social norms and behaviors.

## Contribution

The paper presents a novel modeling approach tailored for social interaction experiments, allowing analysis of dependent data and revealing behavioral patterns over time.

## Key findings

- Ingroup favouritism and reciprocity are observed in the experiments.
- Behavioral strengthening of social norms over time is quantified.
- The method identifies participants with atypical behavior.

## Abstract

Analysis of temporal network data arising from online interactive social experiments is not possible with standard statistical methods because the assumptions of these models, such as independence of observations, are not satisfied. In this paper, we outline a modelling methodology for such experiments where, as an example, we analyse data collected using the Virtual Interaction Application (VIAPPL) --- a software platform for conducting experiments that reveal how social norms and identities emerge through social interaction. We apply our model to show that ingroup favouritism and reciprocity are present in the experiments, and to quantify the strengthening of these behaviours over time. Our method enables us to identify participants whose behaviour is markedly different from the norm. We use the method to provide a visualisation of the data that highlights the level of ingroup favouritism, the strong reciprocal relationships, and the different behaviour of participants in the game. While our methodology was developed with VIAPPL in mind, its usage extends to any type of social interaction data.