Exploring the Intellectual Composition of Academic Research Conferences: Computational Text Analysis of the HICSS Paper Archive from 2017-2022

Simple item page
dc.contributor.authorCogburn, Derrick
dc.contributor.authorOchieng, Theodore
dc.contributor.authorBuehlman Barbeau , Sierra
dc.contributor.authorWong, Haiman
dc.date.accessioned2023-12-26T18:36:29Z
dc.date.available2023-12-26T18:36:29Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.103
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other37631379-c63a-4d2a-b739-79f32898ae31
dc.identifier.urihttps://hdl.handle.net/10125/106480
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: Pathways to Maturity
dc.subjectconference papers
dc.subjectnamed entity recognition
dc.subjectsupervised machine learning.
dc.subjecttext analytics
dc.subjecttopic modeling
dc.titleExploring the Intellectual Composition of Academic Research Conferences: Computational Text Analysis of the HICSS Paper Archive from 2017-2022
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractAcademic research conferences play a critical role in national and international scientific production. For example, the Hawaii International Conference on System Sciences is one of the longest running academic conferences in the world. HICSS consistently produces a wide range of high-quality, peer-reviewed research papers, distributed amongst 10 core tracks, and multiple minitracks. This paper provides a computational method for assessing academic research conferences by exploring the intellectual composition of the HICSS conference asking: what themes are most prevalent across the conference? Are topics identifiable? Can we predict the track of a paper from its abstract? To answer these questions, we analyze the HICSS papers from 2017-2022 (n=5,024). Applying inductive and deductive text-mining techniques, including: “Bag of Words” frequencies, NLP, unsupervised and supervised machine learning, we find several consistent themes and topics over the past five years, as well as meaningful divergence. Finally, the abstract of a paper predicts its track.
dcterms.extent10 pages
prism.startingpage844

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0082.pdf
Size:
2.19 MB
Format:
Adobe Portable Document Format
Download

Collections

Big Data and Analytics: Pathways to Maturity