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Large-scale qualitative-temporal research faces significant data management and analysis challenges due to the size and the textual and temporal nature of the datasets. We propose a systematic methodology that employs visual exploration to produce a purposive sample of a much larger collection of data, followed by a combination of thematic analysis and visualization. This method allows for the preservation of the whole, producing thematic timelines that can be used to elucidate a narrative of incidents or issues as they unfold. We present a step-by-step guide for this methodology and a comprehensive example from the domain of social media analysis to illustrate how it can be used to reveal interesting temporal patterns among tweets relevant to a noteworthy incident. The approach is useful in sport management, particularly for research related to fan behavior, critical incident management, and media framing.
O. Hoeber is with the Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada. Snelgrove and Wood are with the Department of Recreation and Leisure Studies, University of Waterloo, Waterloo, Ontario, Canada. L. Hoeber is with the Faculty of Kinesiology & Health Studies, University of Regina, Regina, Saskatchewan, Canada.