Automated versus Human Engagement: Mapping Cognitive Bias Triggers in Online Discourse

In the digital environment, human attention is frequently guided by cognitive heuristics rather than deliberate evaluation. Since low-credibility narratives often lack substantive factual evidence, their diffusion disproportionally relies on activating these mental shortcut to simulate credibility and capture attention. This study presents a computational framework designed to detect computational triggers through observable data proxies for eight distinct cognitive biases across 3.5 million posts of contested COVID-19 narratives. We demonstrate that automated accounts (bots) embed these triggers more frequently than human users, yielding distinctly source-dependent associations with audience interaction. In bot-authored posts, affective and cognitive dissonance (stance-shifting) triggers are strongly associated with higher engagement, while the deployment of authority and availability (repetition) cues correlates with reduced audience interaction. Furthermore, we identify limits to heuristic compounding: positive engagement correlations with bot-authored content declines when multiple biases are stacked within a single post, whereas human-authored communication remains structurally resilient to high trigger density. By operationalizing psychological heuristics into scalable, measurable data, this work bridges computational social science and cognitive psychology to reveal how source identity (bot/human) shapes the mechanics of information diffusion in digital networks.