Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields

TL;DR

This paper develops an automatic method to detect research values in large-scale CS abstracts, enabling comprehensive analysis of research motivations across subfields and venues over a decade.

Contribution

It introduces a new annotation scheme for research values and builds classifiers to analyze extensive CS literature automatically.

Findings

Successfully classified research values in 226,600 abstracts

Revealed trends and differences in research motivations across subfields

Demonstrated scalability of automatic detection methods

Abstract

The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.