Bots and Moral Panic

Large-language models (LLMs) have abruptly reduced the cost of building social-media bots that converse fluently, evade detection, and flood platforms with low-quality but “original’’ content. Drawing on a case study in which marine scientist Terry Hughes uncovered a network of ChatGPT-generated bots posting about the Great Barrier Reef, this chapter interrogates the epistemic and sociotechnical stakes of this new bot ecology. I argue that the problems such bots pose for truth and trust cannot be understood solely at the level of automated deception; they must also be read through the lens of informational moral panics that shape how societies imagine—and legislate against—digital threats. Situating AI-powered bots within the wider post-truth condition, the chapter shows how anxieties about authenticity, institutional authority and platform decline converge to frame bots as deviant “others’’ responsible for democratic erosion. This framing, I contend, obscures the mundane diversity of bot labour, naturalises platform business models that monetise attention, and fosters cryptonormative judgments that lack explicit normative foundations.

Moving beyond Manichean “good-bot/bad-bot’’ binaries, the chapter proposes a normative agenda that treats bots and LLM-generated content as integral—rather than external—to contemporary media systems. It advocates (1) deflating moral panics through critical education that foregrounds collective, not merely individual, responsibility; (2) aligning regulatory regimes with cross-platform coordination to dampen profit incentives for low-quality automation; and (3) developing situated evaluation frameworks that balance epistemic risk against social benefit. In reframing AI bots from existential threat to sociotechnical artefact, the chapter opens space for constructive, pluralistic responses to the evolving politics of automation, trust and truth.