Abstract
Artificial intelligence (AI) is creating a revolution in business and society at large, as well as challenges for organizations. AI-powered social bots can sense, think and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors' intentions is vital from the management perspective. Drawing from the actor-network theory (ANT), this study investigates human and non-human actors' roles in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre-processing techniques, we applied the bag of words model to a dataset of 30,000 English-language tweets. The present research is among the few studies to use a theory-based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organizations.