Collaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth
by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself …

While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted
collaborative qualitative analysis (CQA)–a process that involves multiple researchers …

Qualitative researchers use tools to collect, sort, and analyze their data. Should qualitative
researchers use large language models (LLMs) as part of their practice? LLMs could …

This study examines the impact of Generative Artificial Intelligence (GenAI) on academic
research, focusing on its application to qualitative and quantitative data analysis. As GenAI …

Data analysts have long sought to turn unstructured text data into meaningful concepts.
Though common, topic modeling and clustering focus on lower-level keywords and require …

Investigative journalists and public defenders conduct the essential work of examining,
reporting, and arguing critical cases around police use-of-force and misconduct. In an ideal …

Recent advancements in general-purpose AI have highlighted the importance of guiding AI
systems towards the intended goals, ethical principles, and values of individuals and …

Sentiment analysis is a vital component of natural language processing (NLP), enabling the
classification of text into positive, negative, or neutral sentiments. It is widely used in …

Just as other disciplines, the humanities explore how computational research approaches
and tools can meaningfully contribute to scholarly knowledge production. Building on …

Practitioners dealing with large text collections frequently use topic models such as Latent
Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) in their projects to …