Crisis Detection in the Age of Digital Communication: The Power of Social Listening as a Method to Identify Corporate Events in Time Series Data

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The increased usage of digital media to exchange information has increased the speed in which corporate crises become known. This has increased the necessity to react to a crisis as quickly as possible. As a result, social listening – i.e. listening to and analysing digital communication – is establishing itself as an instrument for companies to control their own representation in the media. Against this background, different methodological approaches in crisis detection (e.g. outlier detection, t-test and Chow test) were tested regarding their quality. For that, we used a data set created by an AI crawling online sources and analysing the results using a neural network. The findings of this study suggest that crises can be identified quite reliably using existing econometric methods. A simple outlier detection in a time series of the total number of fragments that uses a time frame of one month on each side of a crisis seems to be the best method so far with the method by Chen and Liu being a close second. The results of this study provide a foundational contribution to this field of research and can help companies detect crises as early as possible allowing the management to react appropriately.