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

Claudiu Creanga; Ioachim Lihor; Liviu P. Dinu

Home/Archive/arXiv

arXiv · 2610.03077

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

Claudiu Creanga·Ioachim Lihor·Liviu P. Dinu

View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.CL

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Claudiu Creanga, Ioachim Lihor, Liviu P. Dinu. 2026-10-02. Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models. https://arxiv.org/abs/2610.03077

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ETHER: Aligning Emergent Communication for Hindsight Experience Replay

Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that were actually achieved. However, HER implicitly assumes access to a goal relabelling function and a predicate function that determines whether a goal has been satisfied. These assumptions break down in instruction-following tasks, where goals are expressed in natural language and differ from the state space. We formalize this as the Hindsight Reinforcement Learning problem, which shows the need to jointly learn these functions alongside the RL policy. To address it, we propose ETHER (Emergent Textual Hindsight Experience Replay), an agent that leverages Emergent Communication to learn the goal-relabelling and predicate functions. ETHER uses a referential game (RG) to train a speaker and a listener to develop a grounded, artificial language describing environment states. It partially aligns this emergent language with instruction language using co-occurrence patterns between task instructions and RL observations. We prove that the relabelling and predicate functions that ETHER derives from the RG avoid the degenerate solutions of the Hindsight RL problem, namely trivial predicates and collapsed relabelling functions. Experiments on BabyAI's PickupDist task show that ETHER's learned RG speaker and listener can function as the goal relabelling and predicate functions of HER, improving sample efficiency despite imperfect language alignment. Our work bridges Emergent Communication and goal-conditioned RL, opening the door to wider applications of HER.

cs.CL↗

arXiv2026

Automatic register identification for the open web using multilingual deep learning

This article presents multilingual deep learning models for identifying web registers -- text varieties such as news reports and discussion forums -- across 16 languages. We introduce the Multilingual CORE corpora, which contain over 72,000 documents annotated with a hierarchical taxonomy of 25 registers designed to cover the entire open web. Using multi-label classification, our best model achieves 79% F1 averaged across languages, matching or exceeding previous studies that used simpler classification schemes. This demonstrates that models can perform well even with a complex register scheme at multilingual scale. However, we observe a consistent performance ceiling across all models and configurations. When we remove documents with uncertain labels through data pruning, performance increases to over 90% F1, suggesting that this ceiling stems from inherent ambiguity in web registers rather than model limitations. Analysis of hybrid texts (those combining multiple registers) reveals that the main challenge lies not in classifying hybrids themselves, but in distinguishing hybrid from non-hybrid documents. Multilingual models consistently outperform monolingual ones, particularly for languages with limited training data. Zero-shot performance on unseen languages drops by an average of 7%, though this varies by language (3--8%), indicating that while registers share features across languages, they also retain language-specific characteristics.

cs.CL↗

arXiv2026

Evaluating the Retrieval Robustness of Large Language Models

Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG could also lead to performance degradation due to imperfect retrieval and the model's limited ability to leverage retrieved content. In this work, we evaluate the robustness of LLMs in practical RAG setups (henceforth retrieval robustness). We focus on three research questions: (1) whether RAG is always better than non-RAG; (2) whether more retrieved documents always lead to better performance; and (3) whether document order impacts results. To facilitate this study, we establish a benchmark of 1,891 samples spanning five datasets across three task categories, each with documents retrieved using both sparse and dense retrievers. We introduce three robustness metrics, each corresponding to one research question. Our experiments across 11 LLMs show that models achieve generally high retrieval robustness, but robustness varies substantially across tasks, suggesting that the decision to adopt RAG remains a case-by-case consideration. We further examine four additional prompting strategies that vary how models interact with retrieved documents. We find that Qwen and GPT models suffer notable robustness declines when reasoning is disabled, even on single-hop QA tasks, and that providing retrieved documents as tool responses improves Claude models but hurts Qwen and GPT models, highlighting potential issues of the GPT models regardless of their best overall robustness under vanilla prompting.

cs.CL↗