Gyawali, Nikesh2025-11-122025https://hdl.handle.net/2097/46957Stance detection, a subfield of opinion mining, is an important task in Natural Language Processing (NLP) that involves determining the attitude or position expressed by an author of a text towards a particular topic or claim, generally referred to as target. More specifically, the task involves automatically determining if an author of a text is In-Favor (Positive), Against (Negative), or Neutral towards a given target. Accurate stance detection is crucial for understanding complex social issues, guiding policy decisions, and shaping effective interventions. While the field of NLP has seen significant progress, capturing nuanced stances from diverse and often ambiguous data remains a challenge. Recent developments in Large Language Models (LLMs) have transformed the field by offering unprecedented capabilities in interpreting subtle linguistic cues and context-dependent meanings. This dissertation advances the field of stance detection by utilizing Large Language Models (LLMs) to improve methodologies across multiple critical and socially impactful domains. In this dissertation, we first explore LLM-enhanced approaches to stance detection in the socially controversial and polarizing topics of gun regulation and vaccines. We introduce the GunStance dataset, consisting of social media posts from X (formerly Twitter) posted by X users after major mass shooting events in the United States. By integrating labeled and unlabeled posts, this dataset allows comprehensive exploration of a semi-supervised learning framework in the context of stance detection. We propose a novel hybrid model that combines semi-supervised techniques with LLMs and show that our approach significantly outperforms traditional stance detection approaches. Furthermore, we assemble a large dataset of social media posts from X, capturing the vaccine discourse over a decade. The dataset includes seven years before COVID-19, as well as three COVID-19 years. Leveraging LLMs, social cognition theories and emotional dynamics, we analyze the vaccine dataset to capture the evolving public attitudes towards vaccines before and during the COVID-19 pandemic. Our study reveals increasing polarization and heightened emotional engagement, with a notable rise in vaccine skepticism amid the global health crisis. Expanding into the less controversial but important financial domain, we construct a financial stance detection corpus from annual 10-K reports filed to U.S. Securities and Exchange Commission (SEC) and earnings call transcripts (ECT) by extracting short text fragments relevant to key financial metrics, such as debt, earnings per share (EPS), and sales, and annotating them using LLM-driven methodologies with strict human validation. This financial stance detection corpus facilitates extensive evaluation of LLMs' ability to detect subtle stances towards financial metrics, a task that requires complex reasoning. Our findings demonstrate the effectiveness of LLMs in performing accurate stance detection without extensive labeled data, showcasing their potential for real-world financial analysis applications. Building upon these insights, we also introduce the Modular Prompt Optimization for Stance Detection (MoPrO-SD) framework. This framework utilizes the prompt optimization capabilities of LLMs by breaking down the complex stance detection prompt into modular, optimizable components. Each module is iteratively refined using LLMs as prompt optimizers, leading to an improved prompt that outperforms human-crafted prompts on several stance detection benchmarks. Collectively, this dissertation advances the field of stance detection by providing comprehensive evidence on the use of LLMs to enhance the performance, adaptability, and efficiency of stance detection methodologies across social media posts and financial documents, offering an analytical and scalable framework for informed and nuanced decision-making in an increasingly digital and interconnected world.en-USStance detectionLarge Language Models (LLMs)Dissertation