Documenting Corporate Harm: A Semantic Action Trajectories Approach to the Opioid Industry Document Archive Shared Task

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Social Sciences & Behavioral Studies, Research Methodology & Innovation, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A method for analyzing corporate behavior over time, termed "semantic action trajectories," is presented, specifically applied to the Opioid Industry Document Archive (OIDA). This approach models changes in actor possibility spaces by treating documents as structured actor–action relations. It employs Semantic Role Labeling (SRL) via the Emory Language and Information Toolkit (ELIT) to extract subject–predicate structures from internal industry documents. Subjects are then normalized into actor categories using rule-based heuristics and language model adjudication. Predicate vocabularies are mapped to psycholinguistic categories using the LIWC lexicon. Finally, random forest feature selection and principal component analysis construct a low-dimensional representation of discourse structure across different periods. The analysis reveals systematic shifts in how corporate actors, regulators, clinicians, and patients are positioned, notably showing that corporate entities and their opioid products follow nearly identical semantic trajectories, indicating similar discursive roles. This method offers a scalable way to analyze institutional behavior in large litigation and historical archives.

Key takeaway

For computational social scientists or NLP engineers analyzing large historical or litigation archives, you should consider applying semantic action trajectories to uncover evolving institutional behaviors. This method, which maps actor-action relations to psycholinguistic categories, can reveal subtle shifts in how entities like corporations and their products are discursively positioned over time. You can identify critical periods of change and unexpected convergences in actor roles, informing deeper qualitative analysis or policy recommendations.

Key insights

Semantic action trajectories can reveal systematic shifts in actor positioning and behavior within large document archives.

Principles

• Actor-action relations define discourse.
• Psycholinguistic mapping reveals roles.
• Low-dimensional representations simplify complex discourse.

Method

Semantic Role Labeling extracts subject–predicate structures, which are then normalized, mapped to psycholinguistic categories, and reduced dimensionally to model actor trajectories over time.

In practice

• Analyze corporate litigation documents.
• Track regulatory discourse evolution.
• Identify actor role convergence.

Topics

• Semantic Role Labeling
• Opioid Industry Document Archive
• Computational Social Science
• Discourse Analysis
• Psycholinguistics
• Actor Trajectories

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.