  Crowdfunding is a powerful tool for individuals or organizations seeking
financial support from a vast audience. Despite widespread adoption, managers
often lack information about dynamics of their platforms. Hawkes processes have
been used to represent self-exciting behavior in a wide variety of empirical
fields, but have not been applied to crowdfunding platforms in a way that could
help managers understand the dynamics of users' engagement with the platform.
In this paper, we extend the Hawkes process to capture important features of
crowdfunding platform contributions and apply the model to analyze data from
two donation-based platforms. For each user-item pair, the continuous-time
conditional intensity is modeled as the superposition of a self-exciting
baseline rate and a mutual excitation by preferential attachment, both
depending on prior user engagement, and attenuated by a power law decay of user
interest. The model is thus structured around two time-varying features --
contribution count and item popularity. We estimate parameters that govern the
dynamics of contributions from 2,000 items and 164,000 users over several
years. We identify a bottleneck in the user contribution pipeline, measure the
force of item popularity, and characterize the decline in user interest over
time. A contagion effect is introduced to assess the effect of item popularity
on contribution rates. This mechanistic model lays the groundwork for enhanced
crowdfunding platform monitoring based on evaluation of counterfactual
scenarios and formulation of dynamics-aware recommendations.
