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.

翻译：众筹是个人或组织向广大受众寻求资金支持的有力工具。尽管已得到广泛采用，但管理者往往缺乏对平台动态的了解。霍克斯过程已被用于表示众多实证领域中的自激行为，但尚未以能够帮助管理者理解用户参与平台动态的方式应用于众筹平台。本文扩展了霍克斯过程，以捕捉众筹平台捐款的重要特征，并将该模型应用于分析两个捐赠型平台的数据。对于每个用户-项目对，连续时间条件强度被建模为自激基准率与优先依附互激效应的叠加，两者均依赖于先前的用户参与，并通过用户兴趣的幂律衰减进行衰减。因此，该模型围绕两个时变特征（捐款次数和项目热度）构建。我们估计了控制数千个项目和数万用户数年捐款动态的参数。我们识别出用户捐款流程中的瓶颈，衡量了项目热度的驱动力，并刻画了用户兴趣随时间的衰减。引入传染效应以评估项目热度对捐款率的影响。该机制模型为基于反事实情景评估和动态感知推荐制定的增强型众筹平台监测奠定了基础。