What Intergroup Hostility Research Tells CTOs About the Hidden Risk in Their Recommendation and Moderation Systems

Most content moderation systems are built to catch the obvious: slurs, explicit threats, and overt calls for violence. That framing misses the majority of harmful discourse. A large-scale empirical study mapping hostile rhetoric across 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election found that intergroup hostility operates through a structured sequence of rhetorical mechanisms, most of which look nothing like hate speech in the conventional sense (Gerard et al., arXiv 2026). For CTOs running recommendation or moderation systems at scale, that finding is not an academic curiosity. It is a design constraint.

The Taxonomy Your Moderation Pipeline Is Probably Missing

The research by Gerard et al. identifies six distinct mechanisms through which hostility toward social groups develops in online discourse: boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, and action orientation. These are not synonyms for toxicity. They are functionally different rhetorical moves, each with a different relationship to downstream harm.

Boundary construction, for instance, does not require any hostile language at all. It simply establishes that an outgroup exists and is meaningfully different. That signal is often invisible to classifiers trained on slur lexicons or sentiment polarity. Yet the research shows it is one of the two structural anchors of the entire hostility system, meaning that almost everything else builds on top of it.

The practical implication is that a moderation pipeline calibrated only to detect explicit hostility will systematically miss the early-stage signals that precede escalation. By the time content is flagged, the rhetorical groundwork has already been laid.

How the Escalation Sequence Changes Risk Prioritisation

One of the more operationally significant findings in the Gerard et al. study is that these mechanisms tend to follow a predictable temporal ordering. Boundary construction, negative evaluation, and action orientation tend to appear early in a discourse arc. Dehumanization and threat construction tend to appear later. Scapegoating tends to appear latest of all.

This sequencing matters for how risk should be scored in a recommender system. A post that contains dehumanizing language is not just harmful in isolation. It is a signal that boundary construction and negative evaluation have already occurred upstream in that conversation or community. Treating each post as an independent unit of analysis discards that contextual information entirely.

A system that models escalation state rather than individual post severity would score risk differently at each stage. That is a harder engineering problem, but it is the correct framing of the actual risk surface.

What This Means for Recommender System Design

Recommender systems are not passive conduits. They actively shape which content accumulates engagement, and engagement is the primary fuel for escalation. A post that sits at the boundary construction stage of the hostility sequence may receive high engagement scores because it is group-identity-salient content, which tends to perform well algorithmically. Surfacing it repeatedly accelerates the conditions under which later-stage mechanisms emerge.

The structural finding that boundary construction anchors the hostility system (Gerard et al., arXiv 2026) suggests that recommender systems need explicit dampening signals for content that scores high on group boundary salience, even when that content does not register as hostile under conventional classifiers. That is a policy decision as much as a technical one, and it requires the CTO and the policy team to agree on what the system is optimising for.

Without that alignment, the recommender and the moderation system will work against each other: one surfacing content that builds the conditions for hostility, the other trying to catch the output after the fact.

Building Detection That Maps to the Mechanism Structure

Operationalizing this taxonomy in a production pipeline requires moving from single-label classification toward multi-dimensional signal detection. Each of the six mechanisms identified in the research represents a distinct detection target with its own feature space.

Boundary and Threat Signals

Boundary construction is detectable through linguistic markers of ingroup/outgroup differentiation, including pronoun usage patterns, group-referential language, and framing of collective identity. Threat construction typically involves attribution of harmful intent or capacity to an outgroup, often without explicit hostility markers. Both can be modelled as separate classifiers whose outputs are combined into a composite risk score.

Dehumanization and Action Orientation

Dehumanization is the mechanism most associated with downstream violence in the political science literature, and it tends to appear after the discourse has already progressed through earlier stages. Action orientation, which involves explicit or implicit calls for exclusionary or punitive treatment of an outgroup, is the mechanism most directly connected to mobilisation. Detecting these two signals in isolation is useful. Detecting them in the context of prior boundary and threat signals in the same community or conversation thread is substantially more informative.

Governance Architecture for a Multi-Signal Moderation System

A multi-mechanism detection system introduces new governance requirements that a binary hate speech classifier does not. When a post is flagged for boundary construction rather than explicit hostility, the decision to act on that signal is inherently more contestable. The system needs documented thresholds, human review workflows calibrated to escalation stage, and audit trails that show which mechanism triggered which intervention.

Regulatory exposure is also shifting in this direction. Frameworks that require platforms to demonstrate systemic risk assessment, rather than just reactive content removal, implicitly require the kind of mechanism-level analysis that this research operationalizes. Building the detection capability now, before that regulatory pressure becomes binding, is a more defensible position than retrofitting it under compliance deadlines.

The Gerard et al. framework does not resolve every design question, but it provides something that most moderation architectures currently lack: a theory-backed, empirically validated structure for what hostility actually looks like in real discourse, at scale, across multiple platforms. That is a more reliable foundation for system design than the ad hoc label taxonomies that most teams are currently working from.