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.

Recent work treats the approach as an instrument rather than background: Brookshire, Reiter, Elwert, and Cugliana use polarity arcs to trace life events in eighteenth-century biographical sources; Jessica Witte uses emotional arcs to compare human- and AI-authored stories; and Maria Antoniak, Melanie Walsh, and colleagues use story shapes to study how people explore narrative space through prompting. The method has also moved into disinformation analysis, game design, and multimodal emotion recognition.

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.

What the Rise of AI Means for Narrative Studies

Narrative scholars now engage the argument directly. Alexandra Georgakopoulou uses it in a 2026 Narrative Inquiry study of co-authored personal storytelling, while Stefan Iversen and colleagues draw on it in the same journal to revise positioning analysis for human–machine narration. David P. Rando’s Artificial Fiction carries the discussion into a book-length study, and James Phelan frames the larger AI-and-narrative debate around the essay.

The argument also travels across languages and approaches. Isabelle Wentworth uses it in research on gendered AI narrative in AI & Society, where Omri Asscher also draws on it in a programmatic case for humanities-based AI studies. Jakub Lutostański brings it into Polish narratology, and David Pruneda Sentíes’s “La quimera en la tierra baldía,” published in UNAM’s Revista de Humanidades Digitales, develops its critique of fixed assumptions about what AI can write.

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. Hasan, Greff, and Grissom use the related negation-sensitivity audit as a baseline for evaluating logical inference, bringing the robustness work into technical LLM evaluation. Hedfeld cites the ethics audit in Science and Engineering Ethics to argue that institutional choices, rather than ad hoc corporate discretion, should determine model constraints.

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. Marina Larionova places it within an international-relations account of competition over global AI governance, while Bariach, Schoenegger, Bhaskar, and Suleyman use it to consider how divergent national positions could shape governance of systems perceived as conscious.

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, and Izhaev and Kuteynikov apply it specifically to foundation-model governance. 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 and spiritual-care regulation, and national health systems. Eltohamy and colleagues carry it into research on low-carbon power systems and electric mobility. Across these settings, the comparison functions as a portable analytic framework rather than a one-off citation.

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 use it at FAccT 2026 as the position from which they critique verifiability-based accountability. At the same conference, Lee, Howison, Lee, and Li test its framing against the practices of the r/LocalLLaMA community. Widder, Li, and Lee extend the accountability question into the political economy of local inference.

The Model Openness Framework and a TMLR consensus paper also draw on its analysis. Empirical studies of artistic image-generation ecosystems and the wider open-model ecosystem use the framework to connect release practices with creator, safety, and accountability questions.