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As vertical crowdfunding platforms (e.g., cultural and creative crowdfunding) are gradually gaining widespread attention, it is vital to explore the determinants specific to their success. For this reason, drawing on the signal theory (i.e., combining project cultural background, peer review valence, and face information disclosure), we develop an econometric model to examine the success determinants of cultural and creative crowdfunding (CCCF) projects. A data set with 2877 real CCCF projects obtained from Modian.com is used for analysis. The Natural Language Processing (NLP) approach is used to calculate the sentiment and information entropy of reviews in crowdfunding projects, and an open-source image processing library is used to identify face information. Moreover, an instrumental variable is considered to address endogeneity problems. The empirical results show that cultural background and peer review valence in CCCF projects positively influence crowdfunding success, while initiators' real face information disclosure negatively influences crowdfunding success. Furthermore, the information entropy of peer reviews negatively moderates the influence of peer review valence on crowdfunding success. This research provides a new perspective, i.e., latent features in CCCF, to explore success determinants. In addition, this study contributes to guiding the CCCF platforms in developing appropriate strategies and helps to improve the initiator's chances of successful fundraising. • An econometric model is used to explore special success determinants of CCCF projects based on signal theory. • Natural language processing and computer vision techniques are applied to measure variables. • Cultural background, peer review valence, and initiators' face will affect the crowdfunding success. [ABSTRACT FROM AUTHOR]