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.