Reception

The Shapes of Stories and narrative sentiment analysis

Elkins and Jon Chun established a reproducible methodology for selecting, comparing, validating, and interpreting sentiment models across long-form narrative.

Knight and Rocklage applied emotional arcs to narrative reversals in Science Advances. He, Breithaupt, Kübler, and Hills grounded a study of 25,728 retellings in related methods in Scientific Reports. Researchers associated with the Piper lab and NarraBench have also drawn on this work in computational narrative research.

The methods have also been used outside literary fiction. Harvard Business School researchers applied emotional arcs to customer journeys. Related work appeared in IEEE Transactions on Games. Other researchers adapted the approach to political misinformation and other nontraditional narrative settings.

Middle reading, SentimentArcs, and Beyond Plot

Elkins and Chun’s 2019 work on nonlinear narrative helped establish “middle reading” as a way to move between computational patterns and close interpretation.

Researchers at Aarhus returned to the study in later work on Hemingway and Danish literature. Tilmatine and colleagues adopted its smoothing method in Frontiers in Psychology. Other scholars have cited “middle reading” directly as a digital-humanities methodology.

Jon Chun’s SentimentArcs developed the methodological side of this work further through ensemble comparison of narrative trajectories.

Beyond Plot returned to the larger interpretive question of what sentiment analysis can reveal when narrative structure cannot be reduced to conventional plot. Digital Humanities Now selected the essay as an Editors’ Choice.

Can Sentiment Analysis Reveal Structure in a Plotless Novel? →

In Search of a Translator

In Search of a Translator has received more than 9,000 views on Frontiers since its publication in 2024.

Yan Wang used Elkins’s visual and affective approach in developing a framework for classical Chinese translation. Other researchers have drawn on the work in studies of human and AI translation, including work on the Shahnameh. The article has also entered research on translation pedagogy, including research by Nugraha, Asi, and Fauzan.

Can GPT-3 Pass a Writer’s Turing Test?

Elkins and Chun’s 2020 experiment was the first writer’s Turing test of a large language model.

Floridi and Chiriatti cited its findings within a year. Later researchers returned to the experiment in work on machine psychology, impersonation, literary memorization, authorship, and narrative bias.

It is now the most-cited article in the Journal of Cultural Analytics, with a field-weighted citation impact of 16.71.

Explainable AI for narrative analysis

Chun and Elkins introduced an early explainable-AI workflow for long-form narrative analysis.

Cugurullo and Xu cited the workflow in Policy and Society. Surveys in IEEE Access and Discover Applied Sciences included it in broader accounts of explainable generative AI. Other researchers have used the approach in LREC workshop research and applied work in banking.

Ethical reasoning and AI evaluation

Chun and Elkins developed an ethics-based audit of moral reasoning across deployed large language models that included confidence scoring.

Liu and colleagues adopted that scoring method for value-priority evaluation at COLING 2025. Sowmya and Vasudeva replicated the eight-model audit in IEEE Access. The Edinburgh LLM Ethics Whitepaper also included the work in its discussion of approaches to evaluating model values and ethical judgment.

The associated publication has a field-weighted citation impact of 63.2, more than sixty times the expected citation impact for comparable publications.

Comparative Global AI Regulation

The EU–China–US framework developed by Chun, Schroeder de Witt, and Elkins has become one of the most widely used parts of their governance research.

The comparison appears in research published in Communications of the ACM, PNAS Nexus, Nature Communications, Information Fusion, and comparative-law scholarship.

Its use has become more substantive over time. Prabhakar and colleagues build an evaluation framework for national AI regulation on the three-regime comparison. Malanond and Boonyopakorn use it in developing regulatory lessons for Thailand. A Canadian Centre for Policy Alternatives guide brings the framework into public-facing discussion of national AI governance.

Other researchers have used the framework to study Chinese newsroom AI, healthcare regulation, and advanced-AI governance. Eltohamy and colleagues used it in research on low-carbon energy systems.

Open-source generative AI

The ICML position paper led by Francisco Eiras developed a benefit-risk framework for open-source generative AI and distinguished among different forms of openness.

Paris, Moon, and Guo identified it at FAccT as one of the major frameworks for thinking about model openness. The Model Openness Framework and a TMLR consensus paper also drew on its analysis.

Later empirical work has tested those questions against actual practice. A large-scale study of artistic image-generation ecosystems used the paper to frame creator practices across open models. The work has also entered research on cross-border business development in generative AI.