Measuring Social Influence with Networked Synthetic Control
This work addresses the challenge of quantifying social influence for researchers in social sciences, but it appears incremental as it builds on existing synthetic control and network methods.
The paper tackled the problem of measuring social influence by introducing a method called social value that combines synthetic control with network science, showing through simulation that the generalized friendship paradox holds in certain situations.
Measuring social influence is difficult due to the lack of counter-factuals and comparisons. By combining machine learning-based modeling and network science, we present general properties of social value, a recent measure for social influence using synthetic control applicable to political behavior. Social value diverges from centrality measures on in that it relies on an external regressor to predict an output variable of interest, generates a synthetic measure of influence, then distributes individual contribution based on a social network. Through theoretical derivations, we show the properties of SV under linear regression with and without interaction, across lattice networks, power-law networks, and random graphs. A reduction in computation can be achieved for any ensemble model. Through simulation, we find that the generalized friendship paradox holds -- that in certain situations, your friends have on average more influence than you do.