Abstract
We consider the problem of project success prediction on crowdfunding platforms. Despite the
information in a project profile can be of different modalities such as text, images, and metadata,
most existing prediction approaches leverage only
the text dominated modality. Nowadays rich visual
images have been utilized in more and more project
profiles for attracting backers, little work has been
conducted to evaluate their effects towards success
prediction. Moreover, meta information has been
exploited in many existing approaches for improving prediction accuracy. However, such meta information is usually limited to the dynamics after
projects are posted, e.g., funding dynamics such
as comments and updates. Such a requirement of
using after-posting information makes both project
creators and platforms not able to predict the outcome in a timely manner. In this work, we designed
and evaluated advanced neural network schemes
that combine information from different modalities
to study the influence of sophisticated interactions
among textual, visual, and metadata on project success prediction. To make pre-posting prediction
possible, our approach requires only information
collected from the pre-posting profile. Our extensive experimental results show that the image
features could improve success prediction performance significantly, particularly for project profiles
with little text information. Furthermore, we identified contributing elements