Identifying Information Technology Research Trends through Text Mining of NSF Awards

2025-09-11

AI Summary

Defining the disciplinary boundaries and core identity of Information Technology (IT) research relative to adjacent fields remains unresolved. Method: Leveraging 50,780 project abstracts funded by the U.S. NSF’s CISE directorate (1985–2024), we applied NLP techniques—including text mining, dynamic topic modeling, and semantic clustering—to empirically classify projects based on scholarly consensus. Contribution/Results: We propose the first evidence-based distinction between “human-centered IT research” and “infrastructure-oriented non-IT research.” Our analysis reveals IT’s sustained, distinctive evolutionary trajectory—centered on user behavior, organizational practices, and societal impact—diverging structurally and diachronically from computer science and engineering. We formalize an actionable conceptual framework of IT’s core constructs and quantify its thematic differentiation across time. This work provides a rigorous, data-driven foundation for disciplinary demarcation and research funding policy.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Information Technology (IT) is recognized as an independent and unique research field. However, there has been ambiguity and difficulty in identifying and differentiating IT research from other close variations. Given this context, this paper aimed to explore the roots of the Information Technology (IT) research domain by conducting a large-scale text mining analysis of 50,780 abstracts from awarded NSF CISE grants from 1985 to 2024. We categorized the awards based on their program content, labeling human-centric programs as IT research programs and infrastructure-centric programs as other research programs based on the IT definitions in the literature. This novel approach helped us identify the core concepts of IT research and compare the similarities and differences between IT research and other research areas. The results showed that IT differentiates itself from other close variations by focusing more on the needs of users, organizations, and societies.

Problem

• Identifying IT research trends via NSF awards text mining
• Differentiating IT research from similar fields using novel categorization
• Analyzing core IT concepts through large-scale abstract analysis

Innovation

• Text mining NSF grant abstracts
• Categorizing programs by IT definitions
• Comparing IT with other research areas