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This paper aims to bring to the fore some conditions of possibility surrounding the audit society thesis that might have contributed to construct a sense of distinctiveness around it, thereby facilitating its spread and travel in the accounting research community (and beyond) as a meaningful epistemological agenda.

My argument is that the aura of distinctiveness surrounding the audit society thesis as a noteworthy epistemological agenda relates to outsider influences, concept plasticity, and in-depth persuasiveness. I discuss these three features of distinctiveness through excerpts from the book and my own interpretations, the whole analytic process being circumscribed by a disciplined imagination exercise (Weick, 1989).

While the webs of unpredictability surrounding social life necessarily influenced the travel of the audit society thesis in academia, it seems to me that the thesis’ journey may have been helped by the author’s outsider status, the malleability of the thesis’ key concepts, and the medium (as a book) through which the thesis was articulated (which provided a persuasive platform to underline and demonstrate the depth of the argument).

In an age where journal rankings abound, the audit society thesis constitutes a telling demonstration that one can have a meaningful impact in setting an epistemological agenda and consolidate her/his scholarly reputation through the publication of a scholarly book.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.