Propaganda is a widely used approach for shaping public opinion and disseminating misinformation in news media.While it has recently gained significant attention within the NLP community, research on fine grained propaganda detection remains heavily concentrated in high resource languages.To bridge this gap, we introduce KinyaProp, the first finegrained propaganda dataset of its kind for Kinyarwanda and, to our knowledge, the first such resource created for a Bantu language.Using this dataset, we evaluate whether state-ofthe-art LLMs can function as reliable annotators in a genuinely low resource and culturally grounded setting.Our results show that current multilingual LLMs do not reliably approximate human annotation behavior.Instead, they behave as conservative annotators whose performance is largely limited to lexically explicit cues, substantially under-identifying propaganda and exhibiting extremely low and unstable performance on discourse-level techniques.Our findings highlight an important limitation of recent successes in LLM based annotation reported for high resource languages, demonstrating that such results do not readily transfer to low resource settings, where scalable annotation would be most valuable.We release KinyaProp to support future research on fine grained propaganda detection and to enable more robust evaluation of multilingual models in underrepresented languages.

The preservation and revitalization of endangered and extinct languages is a meaningful endeavor, conserving cultural heritage while enriching fields like linguistics and anthropology. However, these languages are typically low-resource, making their reconstruction labor-intensive and costly. This challenge is exemplified by Nüshu, a rare script historically used by Yao women in China for self-expression within a patriarchal society. To address this challenge, we introduce NüshuRescue, an AI-driven framework designed to train large language models (LLMs) on endangered languages with minimal data. NüshuRescue automates evaluation and expands target corpora to accelerate linguistic revitalization. As a foundational component, we developed NCGold, a 500-sentence Nüshu-Chinese parallel corpus, the first publicly available dataset of its kind. Leveraging GPT-4-Turbo, with no prior exposure to Nüshu and only 35 short examples from NCGold, NüshuRescue achieved 48.69% translation accuracy on 50 withheld sentences and generated NCSilver, a set of 98 newly translated modern Chinese sentences of varying lengths. In addition, we developed FastText-based and Seq2Seq models to further support research on Nüshu. NüshuRescue provides a versatile and scalable tool for the revitalization of endangered languages, minimizing the need for extensive human input. All datasets and code have been made publicly available at https://github.com/ivoryayang/NushuRescue.

Nüshu is an endangered language from Jiangyong County, China, and the world's only known writing system created and used exclusively by women.Recent Natural Language Processing (NLP) work has digitized small Nüshu-Chinese corpora, but the script remains computationally inaccessible due to its handwritten, mixedmedia form and dearth of multimodal resources.We address this gap with two novel datasets: NüshuVision, an image corpus of 500 rendered sentences in traditional vertical, right-to-left orthography, and NüshuStrokes, the first sequential handwriting recordings of all 397 Unicode Nüshu characters by an expert calligrapher.Evaluating five state-of-the-art Chinese Optical Character Recognition (OCR) systems on NüshuVision shows that all fail entirely, each yielding a Character Error Rate (CER) of 1.0.Fine-tuning Microsoft's TrOCR on NüshuVision lowers CER to 0.67, a modest yet meaningful improvement.These contributions establish the first multimodal foundation for Nüshu revitalization and offer a culturally grounded framework for language preservation.

Weicheng Ma, Hefan Zhang, Ivory Yang, Shiyu Ji, Joice Chen, Farnoosh Hashemi, Shubham Mohole, Ethan Gearey, Michael Macy, Saeed Hassanpour, Soroush Vosoughi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Mental manipulation, a significant form of abuse in interpersonal conversations, presents a challenge to identify due to its contextdependent and often subtle nature.The detection of manipulative language is essential for protecting potential victims, yet the field of Natural Language Processing (NLP) currently faces a scarcity of resources and research on this topic.Our study addresses this gap by introducing a new dataset, named MENTALMANIP, which consists of 4, 000 annotated fictional dialogues.This dataset enables a comprehensive analysis of mental manipulation, pinpointing both the techniques utilized for manipulation and the vulnerabilities targeted in victims.Our research further explores the effectiveness of leading-edge models in recognizing manipulative dialogue and its components through a series of experiments with various configurations.The results demonstrate that these models inadequately identify and categorize manipulative content.Attempts to improve their performance by fine-tuning with existing datasets on mental health and toxicity have not overcome these limitations.We anticipate that MENTALMANIP will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations.I just saw Joe.He's here.Well, you don't have to be so dramatic about it.