---
title: "Can AI-Generated Persuasion Be Detected? Persuaficial"
description: "To answer them, they build Persuaficial, a multilingual benchmark of roughly 65k synthetic texts in English, German, Polish… 60 references."
authors: ["Arkadiusz Modzelewski", "Paweł Golik", "Anna Kołos", "Giovanni Da San Martino"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1433/"
doi: "10.18653/v1/2026.acl-long.1433"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/modzelewski-2026-can-ai-generated-persuasion-be/"
---
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# Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences

**Authors:** [Arkadiusz Modzelewski](https://md.research.elamur.ai/free/en/authors/arkadiusz-modzelewski/), Paweł Golik, Anna Kołos and [Giovanni Da San Martino](https://md.research.elamur.ai/free/en/authors/giovanni-da-san-martino/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1433/) · [PDF](https://aclanthology.org/2026.acl-long.1433.pdf) · [DOI 10.18653/v1/2026.acl-long.1433](https://doi.org/10.18653/v1/2026.acl-long.1433) · [OpenAlex](https://openalex.org/W7166888462)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large language models can write convincing persuasive text, which raises the risk of automated propaganda and manipulation. The authors ask two questions. First, is LLM-written persuasion harder for zero-shot LLM detectors to recognise than persuasion written by people? Second, how do the two differ linguistically?

To answer them, they build Persuaficial, a multilingual benchmark of roughly 65k synthetic texts in English, German, Polish, Italian, French and Russian. Human-annotated items from three existing persuasion corpora are given to four LLMs under four controlled prompting strategies:
- plain paraphrasing,
- rewriting to make the persuasion subtler,
- rewriting to make it stronger,
- free generation from a short factual summary.

The detection results are mixed. Blatantly persuasive machine text can actually be easier to flag than human text. Toned-down LLM persuasion, however, consistently lowers detection quality.

A StyloMetrix-based comparison of English texts shows that machine persuasion is denser in content words, uses fewer function words and repeats words less. Human writing tends toward more complex syntax. These results point to concrete weak spots and interpretable cues for building sturdier persuasion detectors.

## Key points
- **Source data:** human-labelled texts come from the SemEval 2023 Task 3 news dataset, DIPROMATS 2024 Task 1 (diplomatic posts on X) and Reddit ChangeMyView. Each language sample has 1,000 texts, split 500 persuasive and 500 non-persuasive. German is the exception, with 420 non-persuasive and 580 persuasive texts.
- **Generators:** two open-weight models (Gemma 3 27b it, Llama 3.3 70B) and two commercial ones (Gemini 2.0 Flash, GPT 4.1 Mini). Combined with the four approaches, this gives 16 generation settings.
- **Summary quality check:** for open-ended generation, LLM-produced factual summaries were checked by two trained annotators on 200 English sentences. About 91.2% were judged factually faithful, and most errors were minor.
- **Linguistic analysis:** 196 StyloMetrix features were ranked by Cohen's d. Content-word share (L_CONT_T) is the most consistent marker, reaching d ≈ 1.41 in one setting. Imperatives and positive adjectives stand out in the intensified and open-ended settings.
- **Release and scope:** the dataset, code and prompts are released on GitHub under CC BY 4.0. All detection is zero-shot, and the linguistic analysis covers English only.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [NASK National Research Institute](https://ror.org/03ajsaw82).

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## Cite

Cite as: Arkadiusz Modzelewski, Paweł Golik, Anna Kołos and Giovanni Da San Martino. 2026. Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{modzelewski2026can,
  title = {{Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences}},
  author = {Arkadiusz Modzelewski, Paweł Golik, Anna Kołos and Giovanni {Da San Martino}},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1433},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1433}
}
```

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*Updated 1 November 2026*