Narrative Prior: Structure and Coherence

Updated 16 November 2025

Narrative prior is a structured expectation or bias that defines how events unfold in narratives, influencing prediction and interpretation.
It employs computational techniques such as event chains, graph structures, and language model metrics to extract and represent narrative coherence.
In social and political contexts, narrative priors drive both coalition formation and epistemic bias, impacting discourse and policy analysis.

A narrative prior is any structured expectation, bias, or schema—often instantiated in computational systems as models, graphs, causal networks, or recontextualized knowledge—about how events tend to unfold in stories or discourse. This concept figures centrally in computational linguistics, cognitive modeling, causality, and social theory, serving as both an explanatory framework (for narrative coherence, event prediction, or coalition formation) and an object of empirical analysis (for bias, distortion, or epistemic authority). Narrative priors may originate from individual cognition, collective memory, algorithmic design, or strategic deployment in social and political communication.

1. Formal Definitions and Theoretical Centers

Across domains, narrative priors are defined as structured sources of expectation about the order, causality, and relevance of events within a narrative. In computational settings, this might be the probabilistic event chains mined from natural corpora (Yao et al., 2018), or the high-dimensional schema encoded in a LLM’s predictive distribution over sentences (Sap et al., 2022). In socio-political theory, narrative priors appear as Bayesian-network causal models mapping actions to outcomes, with auxiliary “story” variables that modulate interpretive focus (Eliaz et al., 2018).

In antagonistic narratives, notably conspiracy texts, the narrative prior is recontextualized external knowledge (scientific papers, news, etc.) that is introduced via citation or hyperlink and then repurposed to serve the text’s worldview, often to imitate epistemic authority while redirecting it for antagonistic aims (Willaert, 27 Apr 2024). The schema-driven expectation of storyflow—whether instantiated in human cognition or learned by neural models—is a narrative prior that dictates which events are expected, surprising, or omitted (Sap et al., 2022).

2. Methodologies for Extraction and Representation

Computational Event Chains and Temporal Ordering

A dominant approach to narrative prior construction leverages narratological principles, notably “double temporality”: the textual order of events in narrative mirrors their chronological reality, permitting algorithmic extraction of temporal event chains (Yao et al., 2018). Seed narrative paragraphs are identified by grammatical and character-structure rules, with subsequent corpus-wide extraction via bootstrapped classifiers. Relations between events are then scored using causal-potential formulas, combining PMI and directed frequency counts. Salience-aware filtering further isolates principal chains of salient events, using kernel-based centrality estimation and discourse parsing to computationally omit background sentences (Zhang et al., 2021).

Graph Structures and Coherence Dependencies

Recent work introduces graph-based narrative priors, where each node represents a passage and directed edges encode “retrospective questions,” probing causal/temporal dependencies not answerable locally but recoverable from prior context (Xu et al., 21 Feb 2024). Node augmentation and edge-based representations support enhanced plot retrieval, recap identification, and context enrichment for question answering.

LLM-Based Metrics

GPT-3 and similar LLMs encode narrative priors at scale, permitting the computation of sequentiality: the contextual boost in likelihood a sentence receives from prior story context versus topic alone. This metric quantifies adherence to prototypical flow and measures divergence at surprising or salient event boundaries (Sap et al., 2022).

In social media analysis, narrative priors are operationalized as citation patterns: clusters of channels or actors are detected via bibliographic coupling of shared links to external knowledge sources, revealing coalition structures organized around jointly recontextualized priors (Willaert, 27 Apr 2024). Community detection (e.g., via Louvain modularity) segments the graph of citations, uncovering ideological, epistemological, and ontological fault lines.

3. Empirical Findings and Quantitative Outcomes

Temporal Event Knowledge

Bootstrapped mining from English Gigaword, BookCorpus, and ICWSM generated 287,000 narrative paragraphs and distilled 19,000 strong event-pairs and 25,000 multi-step chains (Yao et al., 2018). Annotator validation confirmed ≈75% precision for event-pairs. Adding causal-potential features improved temporal relation classification by +1.1% overall (+4.9% for “before” relations) and raised narrative cloze accuracy from 46.67% to 48.83%.

Narrative Flow and Memory

Sequentiality analyses on the Hippocorpus dataset established that imagined stories have higher sequentiality than recalled (autobiographical) ones, with retold memories intermediate. The presence of major, especially surprising events, reduced sequentiality, indicating a local “break” from shared narrative priors (Sap et al., 2022).

Salience-Aware Chains

Filtering for salient events improved ROCStories test accuracy by +2.6% (unsupervised) and by +1.2% in supervised settings; temporal QA F1 gains reached +1.1% (Zhang et al., 2021). Salience-models improved 8-way NewsDiscourse macro-F1 by +3.8%.

Empirical analysis of 77,700 cited sources among 10,000 Telegram channels identified six major narrative coalitions, including science/technology imaginaries, far-right conspiracism, Marxist/anarchist discourses, etc. Cluster composition correlated with epistemic stance and disciplinary origin, demonstrating how recontextualized knowledge (narrative prior) both divides and connects actors (Willaert, 27 Apr 2024).

Narrative Hallucination and Omission in Video LLMs

The NOAH benchmark revealed high rates of hallucination and omission driven by narrative priors in Video LLMs. For example, BLIP-3-Video yielded a caption hallucination rate of 0.888 and omission rate of 0.994; closed-source Gemini 2.5 Flash yielded 0.442 and 0.568, respectively. Event omission rates and QA task performance indicated that many models privilege narrative continuity over visual evidence, with errors magnified by temporal context reduction and composite event insertion (Lee et al., 9 Nov 2025).

In epistemic and political contexts, narrative priors serve as the scaffold upon which narratives—whether scientific, conspiratorial, or policy-advocating—are constructed. Bayesian-network formalizations reveal that narratives encode causal chains between actions and consequences, with equilibrium distributions over narrative-policy pairs determined by anticipatory utility (“net hope”). Perfect DAGs preserve status quo correlations and sustain centrist equilibria; opportunity narratives employing imperfect DAGs enable more extreme polarization by distorting even factual baselines (Eliaz et al., 2018).

On social media, the rapid recontextualization of external sources via hyperlinks enables the formation of discourse (narrative) coalitions, leveraging the authority of shared narratives even as their meaning is subverted (Willaert, 27 Apr 2024).

5. Implementation in AI and NLP Systems

Narrative priors improve or bias the following:

Temporal relation extraction: Textual order-based algorithms paired with salience and discourse filters yield sharper, more predictive event chains (Yao et al., 2018, Zhang et al., 2021).
Coherence modeling: NarCo-style graphs leverage automated retrospective question generation for zero-shot and supervised improvements in plot retrieval and QA, yielding up to +4.7 F1 and +5% QA accuracy over LLM baselines (Xu et al., 21 Feb 2024).
Video description and QA: Models trained for narrative coherence frequently hallucinate or omit events when composite (incongruous) clips break continuity; empirical rates and ablation analyses underscore the need for stricter grounding and novel loss formulations (Lee et al., 9 Nov 2025).
Social knowledge analysis: Bibliometric linking of channels affords scalable coalition identification, clarifying the role of narrative priors in splitting or connecting communities (Willaert, 27 Apr 2024).

6. Implications, Limitations, and Recommendations

Narrative priors confer substantial advantages in fluency, coherence, and domain-specific event prediction, but introduce critical risks of factual distortion, omission, and epistemic bias. In LLMs, the inductive bias toward global continuity can override direct observation, requiring:

Penalty functions for misaligned events and hallucinations,
Curriculum strategies interleaving coherence and direct evidence,
Graph-based or contrastive learning to detect incongruent inserts,
Expanded temporal context or more granular frame sampling to strengthen grounding in external evidence (Lee et al., 9 Nov 2025, Zhang et al., 2021, Xu et al., 21 Feb 2024).

In political and epistemic discourse, deliberate construction and selection of narrative priors shape the topology of coalition, polarization, and sense-making.

7. Cross-Cutting Taxonomy of Narrative Priors

Domain	Formalization	Primary Use
Computational (NLP/AI)	Event chains, LM probabilities, graphs	Event prediction, QA, retrieval
Cognitive/Psycholinguistic	Sequentiality via LM	Flow, episodic vs. schematic recall
Social/Political	Bayesian networks, citation graphs	Policy support, coalition formation
Epistemic/Antagonistic	Recontextualized external knowledge	Authority construction, sense-making

Narrative priors function as structured reservoirs of expectation, enabling inference, prediction, and coalition-building—but also modulation, distortion, and polarization—across computational, cognitive, and sociocultural systems.