Ratna Kandala

Anamta Khan, Ratna Kandala, Deepti et al.

Social media platforms have become primary channels for health information in the Global South. Using gomutra (cow urine) discourse on YouTube in India as a case study, we present a post-facto Large Language Model (LLM)-assisted discourse analysis of 30 multilingual transcripts showing that promotional content blends sacred traditional language with pseudo-scientific claims in ways that sophisticated debunking content itself mirrors, creating a rhetorical register that LLMs, trained predominantly on Western corpora, are systematically ill-equipped to analyse. Varying prompt tone across three LLMs (GPT-4o, Gemini 2.5 Pro, DeepSeek-V3.1), we find that culturally embedded health misinformation does not look like ordinary misinformation, and this cultural obfuscation extends to gendered rhetoric and prompt design, compounding analytical unreliability. Our findings argue that cultural competency in LLM-assisted discourse analysis cannot be retrofitted through prompt engineering alone.

Ratna Kandala, Katie Hoemann

Understanding the nuances in everyday language is pivotal for advancements in computational linguistics & emotions research. Traditional lexicon-based tools such as LIWC and Pattern have long served as foundational instruments in this domain. LIWC is the most extensively validated word count based text analysis tool in the social sciences and Pattern is an open source Python library offering functionalities for NLP. However, everyday language is inherently spontaneous, richly expressive, & deeply context dependent. To explore the capabilities of LLMs in capturing the valences of daily narratives in Flemish, we first conducted a study involving approximately 25,000 textual responses from 102 Dutch-speaking participants. Each participant provided narratives prompted by the question, "What is happening right now and how do you feel about it?", accompanied by self-assessed valence ratings on a continuous scale from -50 to +50. We then assessed the performance of three Dutch-specific LLMs in predicting these valence scores, and compared their outputs to those generated by LIWC and Pattern. Our findings indicate that, despite advancements in LLM architectures, these Dutch tuned models currently fall short in accurately capturing the emotional valence present in spontaneous, real-world narratives. This study underscores the imperative for developing culturally and linguistically tailored models/tools that can adeptly handle the complexities of natural language use. Enhancing automated valence analysis is not only pivotal for advancing computational methodologies but also holds significant promise for psychological research with ecologically valid insights into human daily experiences. We advocate for increased efforts in creating comprehensive datasets & finetuning LLMs for low-resource languages like Flemish, aiming to bridge the gap between computational linguistics & emotion research.

Ratna Kandala, Katie Hoemann

Understanding emotional nuances in everyday language is crucial for computational linguistics and emotion research. While traditional lexicon-based tools like LIWC and Pattern have served as foundational instruments, Large Language Models (LLMs) promise enhanced context understanding. We evaluated three Dutch-specific LLMs (ChocoLlama-8B-Instruct, Reynaerde-7B-chat, and GEITje-7B-ultra) against LIWC and Pattern for valence prediction in Flemish, a low-resource language variant. Our dataset comprised approximately 25000 spontaneous textual responses from 102 Dutch-speaking participants, each providing narratives about their current experiences with self-assessed valence ratings (-50 to +50). Surprisingly, despite architectural advancements, the Dutch-tuned LLMs underperformed compared to traditional methods, with Pattern showing superior performance. These findings challenge assumptions about LLM superiority in sentiment analysis tasks and highlight the complexity of capturing emotional valence in spontaneous, real-world narratives. Our results underscore the need for developing culturally and linguistically tailored evaluation frameworks for low-resource language variants, while questioning whether current LLM fine-tuning approaches adequately address the nuanced emotional expressions found in everyday language use.