  Research on crowdfunding success that incorporates CATA (computer-aided text
analysis) is quickly advancing to the big leagues (e.g., Parhankangas and
Renko, 2017; Anglin et al., 2018; Moss et al., 2018) and is often theoretically
based on information asymmetry, social capital, signaling or a combination
thereof. Yet, current papers that explore crowdfunding success criteria fail to
take advantage of the full breadth of signals available and only very few such
papers examine technology projects. In this paper, we compare and contrast the
strength of the entrepreneur's textual success signals to project backers
within this category. Based on a random sample of 1,049 technology projects
collected from Kickstarter, we evaluate textual information not only from
project titles and descriptions but also from video subtitles. We find that
incorporating subtitle information increases the variance explained by the
respective models and therefore their predictive capability for funding
success. By expanding the information landscape, our work advances the field
and paves the way for more fine-grained studies of success signals in
crowdfunding and therefore for an improved understanding of investor
decision-making in the crowd.

Social and Information Networks;Computers and Society;Human-Computer Interaction;Machine Learning