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Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models — arXiv:2610.03077

Source: arXiv
Original: https://arxiv.org/abs/2610.03077
License (as archived): CC0-1.0
Archived: 2026-10-05T22:51:42.103Z
SHA-256 of the archived text: 463d32cff67dea78f70cf8b3649482cd850501067847e74ef3540ff356dce3f7
Stable URL of this page: https://metraipolis.com/documento/dc_caedb446df92

How to cite

Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models — arXiv:2610.03077 — arXiv, https://arxiv.org/abs/2610.03077 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_caedb446df92 (Metraipolis archive dc_caedb446df92, sha256 463d32cff67dea78f70cf8b3649482cd850501067847e74ef3540ff356dce3f7)

BibTeX

@misc{dccaedb446df92,
  title = {Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models — arXiv:2610.03077},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.03077},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_caedb446df92; sha256 463d32cff67dea78f70cf8b3649482cd850501067847e74ef3540ff356dce3f7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.03077. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. However, identifying specific propaganda techniques presents a significant challenge due to their often subtle nature and reliance on context, making them difficult to distinguish from legitimate persuasive language. Propaganda often involves highlighting certain facts while downplaying or ignoring others to create a desired perception. This biased communication aims to influence attitudes, beliefs, or behaviors towards a particular cause or position. This paper explores advances in detecting propaganda techniques through a comparative analysis of modern language models, using the SemEval-2020 Task 11 dataset. We evaluated both masked language models (based on XLM-RoBERTa or DeBERTa V3) and causal models (from OpenAI, Google, Mistral, Anthropic and Meta), employing two prompting strategies: base and chain-of-thought prompting. Our results demonstrate improvements over state-of-the-art models, with the best-performing MLM achieving an F1 score of 63.18 in technique classification and the best causal model achieving 63.62. We also observed that certain models excel in specific techniques, such as loaded language and name-calling, while struggling with others like bandwagon and black-and-white fallacy. These findings suggest that fine-tuning, ensemble modeling, and the use of larger datasets can further enhance propaganda detection capabilities.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.03077 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-05T22:46:10.569Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).