Background: Big qualitative data analysis is an emerging discipline in qualitative health research and has been used with online posts, open-ended survey responses, and patient health records. Traditional methods of qualitative data analysis can be time-consuming and biased by small sample sizes. The combined strengths of collaborative and participatory methods from rapid research approaches and the efficiency of digital software analyses can mitigate these issues. Aim: We developed the LISTEN method (Collaborative and Digital Analysis of Big Qualitative Data in Time Sensitive Contexts), combining interdisciplinary expertise in collaborative, participatory, and digital methods for big qualitative data analysis. Methods: The LISTEN project iteratively combines findings from a systematic review of peer-reviewed literature and world-wide-web data as well as consultation with stakeholders, collaborative team discussions and text network analysis using digital software. Text and thematic analysis software was used to conduct sentiment analysis and text network analysis of data from academic literature on digital software usage, types of qualitative data, qualitative analysis methods, analysis steps, and citations of notable publications in the field of big qualitative analysis methods. Results: 520 peer-reviewed studies and 37,129 internet posts were systematically reviewed. Web and social media posts referencing large qualitative data sets presented negative sentiments and many posts expressed ambiguity surrounding the categorization of digital and computational methods within the qualitative data analysis discipline. Over 50 types of digital software, and several collaborative qualitative data analysis methods and steps were identified. A LISTEN method manual has been developed to train and support the implementation of the method at three different sites, as well as the development of an interactive living systematic review. Conclusions: The newly developed LISTEN method will provide research teams with the flexibility to triangulate different types of data and combine the strengths of rapid research designs and digital methods.