{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:28:35Z","timestamp":1760059715859,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of State","award":["1841520","2127901"],"award-info":[{"award-number":["1841520","2127901"]}]},{"name":"MITRE Inc.","award":["1841520","2127901"],"award-info":[{"award-number":["1841520","2127901"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1841520","2127901"],"award-info":[{"award-number":["1841520","2127901"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs\u2019 applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses.<\/jats:p>","DOI":"10.3390\/a18070420","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T07:31:57Z","timestamp":1751959917000},"page":"420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7053-7844","authenticated-orcid":false,"given":"Yahya","family":"Masri","sequence":"first","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7183-5166","authenticated-orcid":false,"given":"Zifu","family":"Wang","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"},{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"}]},{"given":"Anusha","family":"Srirenganathan Malarvizhi","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"},{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Samir","family":"Ahmed","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Tayven","family":"Stover","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0525-0071","authenticated-orcid":false,"given":"David W. S.","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4591-483X","authenticated-orcid":false,"given":"Yongyao","family":"Jiang","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3205-8464","authenticated-orcid":false,"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-4877","authenticated-orcid":false,"given":"Qian","family":"Liu","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1990-2840","authenticated-orcid":false,"given":"Mathieu","family":"Bere","sequence":"additional","affiliation":[{"name":"Jimmy and Rosalynn Carter School for Peace and Conflict Resolution, George Mason University, Arlington, VA 22201, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5495-7279","authenticated-orcid":false,"given":"Daniel","family":"Rothbart","sequence":"additional","affiliation":[{"name":"Jimmy and Rosalynn Carter School for Peace and Conflict Resolution, George Mason University, Arlington, VA 22201, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-0069","authenticated-orcid":false,"given":"Dieter","family":"Pfoser","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7768-4066","authenticated-orcid":false,"given":"Chaowei","family":"Yang","sequence":"additional","affiliation":[{"name":"NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA"},{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"ref_1","unstructured":"Croicu, M. (2024). Deep Active Learning for Data Mining from Conflict Text Corpora. arXiv."},{"key":"ref_2","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_3","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2025, July 03). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"ref_4","unstructured":"Wang, Z., Masri, Y., Malarvizhi, A.S., Stover, T., Ahmed, S., Wong, D., Jiang, Y., Li, Y., Bere, M., and Rothbart, D. Optimizing Context-Based Location Extraction by Tuning Open-Source LLMs with RAG. Int. J. Digit. Earth."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, Y., Li, Y., Kakkar, D., Guan, W., Ji, W., Cain, J., Lan, H., Sha, D., and Liu, Q. (2022). Public Opinions on COVID-19 Vaccines\u2014A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method. Vaccines, 10.","DOI":"10.3390\/vaccines10091486"},{"key":"ref_6","unstructured":"Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P.S., and He, L. (2021). A Survey on Text Classification: From Shallow to Deep Learning. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lavanya, P., and Sasikala, E. (2021, January 13\u201314). Deep Learning Techniques on Text Classification Using Natural Language Processing (NLP) In Social Healthcare Network: A Comprehensive Survey. Proceedings of the 2021 3rd International Conference on Signal Processing and Communication (ICPSC), Coimbatore, India.","DOI":"10.1109\/ICSPC51351.2021.9451752"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Z., Pang, Y., Lin, Y., and Zhu, X. (2024). Adaptable and Reliable Text Classification Using Large Language Models. arXiv.","DOI":"10.1109\/ICDMW65004.2024.00015"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"04021066","DOI":"10.1061\/(ASCE)NH.1527-6996.0000547","article-title":"Rapid Perception of Public Opinion in Emergency Events through Social Media","volume":"23","author":"Chen","year":"2022","journal-title":"Nat. Hazards Rev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sufi, F. (2024). Advances in Mathematical Models for AI-Based News Analytics. Mathematics, 12.","DOI":"10.3390\/math12233736"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34046","DOI":"10.1109\/ACCESS.2022.3162614","article-title":"A Long-Text Classification Method of Chinese News Based on BERT and CNN","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"145386","DOI":"10.1109\/ACCESS.2023.3345414","article-title":"Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology","volume":"11","author":"Fatemi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_13","unstructured":"Wang, Y., Qu, W., and Ye, X. (2024). Selecting Between BERT and GPT for Text Classification in Political Science Research. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1080\/17538947.2023.2233488","article-title":"Adopting GPU Computing to Support DL-Based Earth Science Applications","volume":"16","author":"Wang","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100059","DOI":"10.1016\/j.nlp.2024.100059","article-title":"Recent Advancements and Challenges of NLP-Based Sentiment Analysis: A State-of-the-Art Review","volume":"6","author":"Jim","year":"2024","journal-title":"Nat. Lang. Process. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"144","DOI":"10.9734\/jamcs\/2023\/v38i101832","article-title":"SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing","volume":"38","author":"Oyeyemi","year":"2023","journal-title":"J. Adv. Math. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, L., Liu, B., Lin, Q., Zhao, X., and Che, C. (2024). Semantic Similarity Matching for Patent Documents Using Ensemble BERT-Related Model and Novel Text Processing Method. arXiv.","DOI":"10.12720\/jait.15.3.446-450"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, S., and Liu, B. (2018). Deep Learning for Sentiment Analysis: A Survey. arXiv.","DOI":"10.1002\/widm.1253"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","article-title":"Machine Learning in Automated Text Categorization","volume":"34","author":"Sebastiani","year":"2002","journal-title":"ACM Comput. Surv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1080\/13658816.2017.1357819","article-title":"A Comprehensive Methodology for Discovering Semantic Relationships among Geospatial Vocabularies Using Oceanographic Data Discovery as an Example","volume":"31","author":"Jiang","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, M., Huang, Q., Qin, H., Scheele, C., and Yang, C. (2020). Deep Learning for Real-Time Social Media Text Classification for Situation Awareness\u2013Using Hurricanes Sandy, Harvey, and Irma as Case Studies. Social Sensing and Big Data Computing for Disaster Management, Routledge.","DOI":"10.4324\/9781003106494-3"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_24","unstructured":"Graves, A. (2014). Generating Sequences with Recurrent Neural Networks. arXiv."},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Patwardhan, N., Marrone, S., and Sansone, C. (2023). Transformers in the Real World: A Survey on NLP Applications. Information, 14.","DOI":"10.3390\/info14040242"},{"key":"ref_27","unstructured":"Ansar, W., Goswami, S., and Chakrabarti, A. (2024). A Survey on Transformers in NLP with Focus on Efficiency. arXiv."},{"key":"ref_28","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models Are Few-Shot Learners. arXiv."},{"key":"ref_29","unstructured":"Alaparthi, S., and Mishra, M. (2020). Bidirectional Encoder Representations from Transformers (BERT): A Sentiment Analysis Odyssey. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhou, X., Fan, J., Xie, X., and Zhu, S. (2024). Can Bidirectional Encoder Become the Ultimate Winner for Downstream Applications of Foundation Models?. arXiv.","DOI":"10.32388\/EV7DDP"},{"key":"ref_31","unstructured":"Aletras, N., Androutsopoulos, I., Barrett, L., Goanta, C., and Preotiuc-Pietro, D. (2021). Effectively Leveraging BERT for Legal Document Classification. Natural Legal Language Processing Workshop 2021, Association for Computational Linguistics."},{"key":"ref_32","first-page":"143","article-title":"Sentiment Analysis in Social Media: Leveraging BERT for Enhanced Accuracy","volume":"2","author":"Wu","year":"2024","journal-title":"J. Ind. Eng. Appl. Sci."},{"key":"ref_33","first-page":"2279","article-title":"Enhancing Text Classification Using BERT: A Transfer Learning Approach","volume":"28","author":"Naeem","year":"2024","journal-title":"Comput. Sist."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, Y. (2024). A Study on News Headline Classification Based on BERT Modeling, Atlantis Press.","DOI":"10.2991\/978-94-6463-540-9_35"},{"key":"ref_35","unstructured":"Bedretdin, \u00dc. (2025, June 24). Supervised Multi-Class Text Classification for Media Research: Augmenting BERT with Topics and Structural Features. Available online: https:\/\/helda.helsinki.fi\/items\/f02c65c9-f449-4fc7-a4ac-2ad23d3cea93."},{"key":"ref_36","first-page":"279","article-title":"An Automated Text Document Classification Framework Using BERT","volume":"14","author":"Shah","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_37","unstructured":"Petridis, C. (2024). Text Classification: Neural Networks VS Machine Learning Models VS Pre-Trained Models. arXiv."},{"key":"ref_38","unstructured":"OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4 Technical Report. arXiv."},{"key":"ref_39","unstructured":"Mao, R., Chen, G., Zhang, X., Guerin, F., and Cambria, E. (2024). GPTEval: A Survey on Assessments of ChatGPT and GPT-4. arXiv."},{"key":"ref_40","unstructured":"Mu, Y., Wu, B.P., Thorne, W., Robinson, A., Aletras, N., Scarton, C., Bontcheva, K., and Song, X. (2024). Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1017\/S1351324923000438","article-title":"Improving Short Text Classification with Augmented Data Using GPT-3","volume":"30","author":"Balkus","year":"2024","journal-title":"Nat. Lang. Eng."},{"key":"ref_42","first-page":"1","article-title":"Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","volume":"55","author":"Liu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A Next-Generation Hyperparameter Optimization Framework. arXiv.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_44","unstructured":"Johnson, J., Douze, M., and J\u00e9gou, H. (2017). Billion-Scale Similarity Search with GPUs. arXiv."},{"key":"ref_45","unstructured":"Brandt, P.T., Alsarra, S., D\u2019Orazio, V.J., Heintze, D., Khan, L., Meher, S., Osorio, J., and Sianan, M. (2024). ConfliBERT: A Language Model for Political Conflict. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/420\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:06:22Z","timestamp":1760033182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/420"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["a18070420"],"URL":"https:\/\/doi.org\/10.3390\/a18070420","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,7,8]]}}}