Information disorder—characterized by contradictory claims within digital information environments—undermines democratic deliberation in ways that traditional misinformation research cannot adequately capture. We develop a scalable framework for measuring disorder based on semantic relationships between information units (infons), without requiring truth adjudication. Applying large language models to classify relationships among 969,207 Facebook posts from 38,149 accounts during the 2020 U.S. presidential election period (October 26–December 1, 2020), we construct temporal networks of agreement and disagreement.

Figure 1. Temporal dynamics of disagreement across general and partisan samples.

Our findings reveal three key patterns. First, internal disagreement increased sharply following Election Day, with Republican-affiliated accounts exhibiting substantially higher disorder than Democrat accounts (see Figure 1). Second, stance toward election legitimacy diverged markedly along partisan lines: Democrat accounts maintained consistent agreement that the election was conducted fairly, while Republican accounts exhibited sustained disagreement. Third, and most critically, posts expressing skepticism toward election fairness achieved significantly greater viral reach than those affirming it across all partisan contexts, suggesting that platform dynamics systematically amplified disorder-generating content (see Figure 2).

These findings demonstrate the utility of network-based measures for diagnosing information ecosystem health and reveal how algorithmic amplification may exacerbate democratic information crises. Our framework provides policymakers and platform designers with actionable metrics for assessing information environment quality without requiring contested judgments about truth.

Figure 2. Association between share count and stance toward election fairness across partisan samples.

Keywords: information disorder, computational social science, LLM, large language models, social media, electoral integrity, platform governance

What is the Undersphere?

We define the Undersphere as a networked space of creative experimentation that exists outside formal markets and institutions — places like GitHub, Reddit (e.g., r/StableDiffusion), and Hugging Face. Here, AI isn’t just consumed; it’s remixed, repurposed, and pushed in unexpected directions. These communities aren’t necessarily political, but their outcomes often are.

A notorious example is the rapid rise of deepfake pornography, which emerged not from corporate labs, but from creative subcultures operating in these loosely regulated, experimental spaces. Today, more than 98% of all deepfake videos online fall into this category.

What’s the Risk?

The risks posed by the Undersphere aren’t just technical — they’re democratic. Generative AI used in these contexts challenges core principles like consent, privacy, and truth. Yet existing regulatory responses (like the EU AI Act) are structured around intended use, with little room to manage the messier, more creative misuse of these tools.

We argue that risk is not static or predictable. Generative AI’s diffusion across social media and developer communities creates complex, cascading, and systemic risks — exactly the kind of challenges traditional frameworks struggle to contain.

Climate Governance as a Model for AI

To better respond to these challenges, we propose that AI governance take inspiration from climate governance — which has evolved to deal with uncertain, distributed, and long-term risks. That means:

• Shifting from rigid regulation to adaptive governance
• Accepting uncertainty and incomplete knowledge as a feature, not a bug
• Focusing on networks of risk, not isolated use cases
• Engaging diverse voices to anticipate unintended harms

This approach, we argue, is more likely to protect the public and the democratic fabric of our societies than reactive bans or usage-based categorisations.

From LOAB to Legislation

One striking case we analyse is the viral AI creation LOAB — a haunting, unintended output of text-to-image models, discovered and shared through Reddit. The community around LOAB exemplifies the Undersphere: highly creative, technically proficient, yet operating entirely outside institutional oversight.

When these practices intersect with social platforms and public discourse, the results can be transformative or catastrophic — and governance must prepare for both.

Wikibase, a free knowledge graph software developed by the Wikimedia Foundation, powers Wikidata by centralising and structuring information into a graph database for consistency and machine readability. Our upcoming book Collaborative Use of Wikibase for Interdisciplinary Mixed-Methods Research outlines how research teams can utilise Wikibase to manage, integrate, and analyse complex data collaboratively. By facilitating data linking, ontology design, and collaborative editing, Wikibase becomes an essential tool for interdisciplinary research projects, bridging qualitative and quantitative methods seamlessly.