Peer influence in large dynamic network: quasi-experimental evidence from scratch

We analyze peer influence of production and consumption of projects in the Scratch community, an online platform developed by MIT Media Lab, where users collectively learn to program by creating and sharing projects. Scratchers can follow others’ activities on the platform; in the followers network, we investigate if Scratchers’ production popularity (determined by others) and consumption preference (self determined) are influenced by whom they follow on the platform (peers). Several mechanisms established in the literature like homophily, selection, peer influence, own behavioural tendency, reciprocated ties, and particular contexts can lead to observations of behavioural clustering in a social network like Scratch, and therefore isolating peer influence from other mechanisms is a challenging task. In this study, we measure peer influence in the Scratch community after accounting for such alternative confounding mechanisms. There are two key steps we follow to estimate peer influence of a behaviour. First, at a given time, we create experimental and control groups such that the peers’ behaviour under investigation can be justified as a random assignment. To do so we exactly match Scratchers’ personal and network attributes in both groups such that Scratchers in the experimental group have peers with higher degree of the behaviour under study compared to the control group, and all other attributes of Scratchers are balanced across both groups. Second, conditional on all activities up to this time (as captured by the attributes), we measure peer influence as the difference in Scratchers’ personal behavioural changes in subsequent periods across the two groups.