Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
American Accounting Association
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/93445 |
| _version_ | 1848765735215562752 |
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| author | Cao, June Gu, Z. Hasan, I. |
| author_facet | Cao, June Gu, Z. Hasan, I. |
| author_sort | Cao, June |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research. |
| first_indexed | 2025-11-14T11:39:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-93445 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:39:58Z |
| publishDate | 2023 |
| publisher | American Accounting Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-934452023-10-25T07:30:39Z Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach Cao, June Gu, Z. Hasan, I. This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research. 2023 Journal Article http://hdl.handle.net/20.500.11937/93445 10.2308/JIAR-2021-073 American Accounting Association restricted |
| spellingShingle | Cao, June Gu, Z. Hasan, I. Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title | Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title_full | Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title_fullStr | Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title_full_unstemmed | Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title_short | Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach |
| title_sort | exploring accounting research topic evolution: an unsupervised machine learning approach |
| url | http://hdl.handle.net/20.500.11937/93445 |