Given the emerging prominence of digital influences across all industries, it seems likely entrepreneurial ventures that embrace digital technologies as core to their business will become even more ubiquitous in the future. However, the relatively nascent literature on digital entrepreneurship has become splintered as numerous areas are divergent in focus and lacking in shared understanding of core concepts. The need for improved understanding and more singular conceptualisations in digital entrepreneurship has served as the motivation for this study. Hence, this study aims to bring cohesion to the digital entrepreneurship literature through an automated content analysis that identifies common themes and concepts present in the literature. The automated content analysis was conducted using the machine-based lexical analysis tool, Leximancer. The data set used for this study is made up of digital entrepreneurship journal articles collected through the Scopus database. In total, a corpus of 576 highly ranked academic journal articles was assembled (A*, A, and B according to the ABDC ranking list, 2016). The insights from this study provide a strong basis for discussion, critique and guidance for future research. This study contributes to the literature by resolving conflicts and disagreements through machine-based lexical analysis of the digital entrepreneurship literature. Contributions include highlighting the importance of network effects and network structures for digital entrepreneurial success. The findings also have important implications for both digital entrepreneurs and researchers, as new areas of scholarly interest have been identified for future research.