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

arXiv: https://arxiv.org/abs/2406.07293 · HTML: https://mlsanity.com/paper/2406.07293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [Simulating Misinformation Vulnerabilities With Agent Personas](https://mlsanity.com/paper/2511.04697/) (2025)
- [BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots](https://mlsanity.com/paper/2601.06154/) (2026)
- [What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics](https://mlsanity.com/paper/2501.00855/) (2025)

## Abstract (arXiv)

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.