Part of the SIML Media Literacy Course Series

This course module introduces learners to the practical skills needed to identify automated accounts (“bots”) in digital environments. In modern online spaces, conversations are often shaped not only by real individuals, but also by automated systems designed to post, respond, and amplify content at scale. Understanding how these systems operate is an essential part of navigating today’s information landscape.

Within the framework of the CRIBSRAC Framework developed by the Social Institute for Media Literacy, spotting bots is not just about detecting fake accounts—it is about understanding how automated participation can influence perception, engagement, and the appearance of consensus in public discourse.

This module teaches learners to observe behavioral patterns rather than rely on single indicators. Automated accounts often display repetitive posting behavior, limited contextual awareness, or unusually consistent activity patterns that do not reflect normal human rhythms. They may also engage in amplification strategies, where the same message is repeated across multiple accounts or platforms to increase visibility and perceived credibility.

Learners will also examine structural indicators such as account age, interaction quality, and engagement style. Bots often produce interactions that feel generic, overly broad, or disconnected from the ongoing conversation. However, the focus of this course is not on assumptions, but on pattern recognition—understanding when multiple signals together suggest automation rather than drawing conclusions from a single trait.

By the end of this module, learners will be able to approach digital conversations with greater awareness of how automated systems can shape online narratives. The goal is not skepticism toward every account, but the development of informed attention—recognizing when participation may be engineered and how that influences what appears to be popular opinion or consensus online.