Stance detection is an evolving opinion mining research area motivated by the vast increase
in the variety and volume of user-generated content. In this regard, considerable research …

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

Language models (LMs) are pretrained on diverse data sources, including news, discussion
forums, books, and online encyclopedias. A significant portion of this data includes opinions …

As polarization continues to rise among both the public and the news media, increasing
attention has been devoted to detecting media bias. Most recent work in the NLP community …

Modeling political actors is at the core of quantitative political science. Existing works have
incorporated contextual information to better learn the representation of political actors for …

Recent research has offered insights into the extraordinary capabilities of Large Multimodal
Models (LMMs) in various general vision and language tasks. There is growing interest in …

This study explores how natural language processing (NLP) can supplement content
analyses of political documents, particularly the manifestos of political parties. NLP is …

By design, large language models (LLMs) are static general-purpose models, expensive to
retrain or update frequently. As they are increasingly adopted for knowledge-intensive tasks …

Recently, there has been a growing interest in analyzing user-generated text to understand
opinions expressed on social media. In NLP, this task is known as stance detection, where …

In recent years, large language models (LLMs) have been widely adopted in political
science tasks such as election prediction, sentiment analysis, policy impact assessment, and …