This study conducted a lexicon-based content analysis of building information modeling (BIM) logs from four major BIM authoring tools and four custom-developed BIM loggers to understand whether the BIM logs satisfy the information requirements for various BIM log mining use cases, as well as to assess their potential for future development and research. First, through a critical review of previous studies, 19 different ways of using BIM logs were identified, including authoring and collaborative pattern discovery, authoring process modeling, collaboration pattern analysis, command predictions, and team optimization; however, most of the uses concerned process discovery. The analysis also revealed that BIM log mining has mainly been used for the design phase, with a few examples of being used for the construction phase. For BIM log mining, various techniques ranging from simple frequency analyses via social network analyses to advanced pattern discovery were deployed. In terms of BIM log sources, native BIM logs from Revit were dominantly used almost in all studies, aside from a few studies that used custom-developed BIM logs. The content analysis of BIM logs showed that the contents of native BIM logs provided by major BIM authoring tools varied, but commonly lacked model-element-specific information; this limitation prevents in-depth analyses of BIM processes. Overall, the current disproportionate focus on process discovery in the design phase of BIM log mining suggests that the application of BIM log mining is still in its early stages and holds significant potential for other project phases if adding model-element-specific information is incorporated.