{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:49:18Z","timestamp":1779101358156,"version":"3.51.4"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>The surge in advancements in large language models (LLMs) has expedited the generation of synthetic text imitating human writing styles. This, however, raises concerns about the potential misuse of synthetic textual data, which could compromise trust in online content. Against this backdrop, the present research aims to address the key challenges of detecting LLMs-generated texts. In this study, we used ChatGPT (v 3.5) because of its widespread and capability to comprehend and keep conversational context, allowing it to produce meaningful and contextually suitable responses. The problem revolves around the task of discerning between authentic and artificially generated textual content. To tackle this problem, we first created a dataset containing both real and DeepFake text. Subsequently, we employed transfer-learning (TL) and conducted DeepFake-detection utilizing SOTA large pre-trained LLMs. Furthermore, we conducted validation using benchmark datasets comprising unseen data samples to ensure that the model's performance reflects its ability to generalize to new data. Finally, we discussed this study's theoretical contributions, practical implications, limitations and potential avenues for future research, aiming to formulate strategies for identifying and detecting large-generative-models\u2019 produced texts. The results were promising, with accuracy ranging from 94% to 99%. The comparison between automatic detection and the human ability to detect DeepFake text revealed a significant gap in the human capacity for its identification, emphasizing an increasing need for sophisticated automated detectors. The investigation into AI-generated content detection holds central importance in the age of LLMs and technology convergence. This study is both timely and adds value to the ongoing discussion regarding the challenges associated with the pertinent theme of \"DeepFake text detection\", with a special focus on examining the boundaries of human detection.<\/jats:p>","DOI":"10.1145\/3708889","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T11:12:11Z","timestamp":1734606731000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Human vs. Machine: A Comparative Study on the Detection of AI-Generated Content"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-8589","authenticated-orcid":false,"given":"Amal","family":"Boutadjine","sequence":"first","affiliation":[{"name":"Department of Computer Science, Universit\u00e9 Ferhat Abbas, Setif, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8348-7146","authenticated-orcid":false,"given":"Fouzi","family":"Harrag","sequence":"additional","affiliation":[{"name":"Computer Sciences, Universite Ferhat Abbas, Setif, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0823-8390","authenticated-orcid":false,"given":"Khaled","family":"Shaalan","sequence":"additional","affiliation":[{"name":"Department of Informatics, British University - Faculty of Engineering and IT, Dubai, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS58326.2023.10137767"},{"key":"e_1_3_3_3_2","unstructured":"Z. Alyafeai M. S. AlShaibani and I. Ahmad. 2020. A survey on transfer learning in natural language processing. arXiv preprint arXiv:2007.04239. Retrieved from https:\/\/arxiv.org\/abs\/2007.04239"},{"key":"e_1_3_3_4_2","unstructured":"W. Antoun F. Baly and H. Hajj. 2020. AraBERT: Transformer-based model for arabic language understanding. arXiv preprint arXiv:2003.00104. Retrieved from https:\/\/arxiv.org\/abs\/2003.00104"},{"key":"e_1_3_3_5_2","unstructured":"W. Antoun V. Mouilleron B. Sagot and D. Seddah. 2023. Towards a robust detection of language model generated text: Is ChatGPT that easy to detect? arXiv preprint arXiv:2306.05871. Retrieved from https:\/\/arxiv.org\/abs\/2306.05871"},{"key":"e_1_3_3_6_2","unstructured":"P. Azunre. 2021. Transfer learning for natural language processing. Simon and Schuster. New York NY."},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-3963-3_7"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3655103.3655106"},{"key":"e_1_3_3_9_2","first-page":"1","article-title":"Language models are few-shot learners\u2014special version","year":"2020","unstructured":"T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, and D. Amodei. 2020. Language models are few-shot learners\u2014special version. Conference on Neural Information Processing Systems (NeurIPS 2020), (NeurIPS), 1\u201325.","journal-title":"Conference on Neural Information Processing Systems (NeurIPS 2020)"},{"key":"e_1_3_3_10_2","article-title":"How can we combat the worrying rise in deepfake content?","author":"Bueermann G.","year":"2023","unstructured":"G. Bueermann and N. Perucica. 2023. How can we combat the worrying rise in deepfake content?\u2019, World Economic Forum, 1 January, Retrieved from https:\/\/www.weforum.org\/agenda\/2023\/05\/how-can-we-combat-the-worrying-rise-in-deepfake-content\/","journal-title":"World Economic Forum"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110605"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmal.2023.100068"},{"key":"e_1_3_3_13_2","article-title":"Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: The case of five AI content detection tools","author":"Chaka C.","year":"2023","unstructured":"C. Chaka. 2023. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: The case of five AI content detection tools. Journal of Applied Learning and Teaching 6, 2 (2023).","journal-title":"Journal of Applied Learning and Teaching"},{"key":"e_1_3_3_14_2","unstructured":"S. Chakraborty A. S. Bedi S. Zhu B. An D. Manocha and F. Huang. 2023. On the possibilities of ai-generated text detection. arXiv preprint arXiv:2304.04736. Retrieved from https:\/\/arxiv.org\/abs\/2304.04736"},{"key":"e_1_3_3_15_2","first-page":"1","article-title":"Short term photovoltaic output prediction based on similar day matching and TCN attention","year":"2020","unstructured":"Y. Chen, M. Wen, K. Zhang, and S. Yu. 2020. Short term photovoltaic output prediction based on similar day matching and TCN attention. Electr. Meas. Instrum, 1\u20139.","journal-title":"Electr. Meas. Instrum"},{"key":"e_1_3_3_16_2","doi-asserted-by":"crossref","unstructured":"R. Collobert and J. Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning. 160\u2013167.","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"e_1_3_3_18_2","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume":"1","year":"2019","unstructured":"J. D. M. W. C. Kenton and L. K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019\u20132019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\u2013Proceedings of the Conference, 1 (Mlm), 4171\u20134186.","journal-title":"NAACL HLT 2019\u20132019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\u2013Proceedings of the Conference"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105964"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104076"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40979-023-00140-5"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0251415"},{"key":"e_1_3_3_23_2","doi-asserted-by":"crossref","unstructured":"L. Fan L. Li Z. Ma S. Lee H. Yu and L. Hemphill. 2024. A bibliometric review of large language models research from 2017 to 2023. ACM Transactions on Intelligent Systems and Technology 15 5 (2024) 1\u201325.","DOI":"10.1145\/3664930"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106597"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00302"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3501401"},{"key":"e_1_3_3_27_2","unstructured":"F. Harrag M. Debbah K. Darwish and A. Abdelali. 2021. BERT transformer model for detecting arabic GPT2 auto-generated tweets. arXiv preprint arXiv:2101.09345. Retrieved from https:\/\/arxiv.org\/abs\/2101.09345"},{"key":"e_1_3_3_28_2","first-page":"187","article-title":"Aspect-based sentiment analysis using BERT","author":"Hoang M.","year":"2019","unstructured":"M. Hoang, O. Alija Bihorac, and J. Rouces. 2019. Aspect-based sentiment analysis using BERT. Proceedings of the 22nd Nordic Conference on Computational Linguistics, 187\u2013196. Available at: https:\/\/aclanthology.org\/W19-6120\/","journal-title":"Proceedings of the 22nd Nordic Conference on Computational Linguistics"},{"key":"e_1_3_3_29_2","unstructured":"R. Kumar and M. Mindzak. 2023. Distinguishing human generated text from ChatGPT generated text using machine learning. Available at: http:\/\/arxiv.org\/abs\/2306.01761"},{"key":"e_1_3_3_30_2","article-title":"Why we need a better definition of \u2018deepfake\u2019","author":"Vincent J.","year":"2018","unstructured":"J. Vincent. 2018. Why we need a better definition of \u2018deepfake\u2019. The Verge, 22.. (Accessed on 2\/15\/2023). Retrieved from https:\/\/www.theverge.com\/2018\/5\/22\/17380306\/deepfake-definition-ai-manipulation-fake-news","journal-title":"The Verge, 22.. (Accessed on 2\/15\/2023)"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/math11153400"},{"key":"e_1_3_3_32_2","first-page":"1","article-title":"Paraphrasing evades detectors of AI-generated text","year":"2024","unstructured":"K. Krishna, Y. Song, M. Karpinska, J. Wieting, and M. Iyyer. 2024. Paraphrasing evades detectors of AI-generated text. But Retrieval is an Effective Defense\u2019, (NeurIPS), 1\u201332.","journal-title":"But Retrieval is an Effective Defense"},{"key":"e_1_3_3_33_2","unstructured":"T. Kumarage J. Garland A. Bhattacharjee K. Trapeznikov S. Ruston and H. Liu. 2023. Stylometric detection of AI-generated text in twitter timelines 1\u201313. Available at: http:\/\/arxiv.org\/abs\/2303.03697"},{"key":"e_1_3_3_34_2","first-page":"156","article-title":"Temporal convolutional networks for action segmentation and detection","author":"Lea C.","year":"2017","unstructured":"C. Lea, M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager. 2017. Temporal convolutional networks for action segmentation and detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 156\u2013165.","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_3_35_2","unstructured":"B. Li Y. He and W. Xu. 2021. Cross-lingual named entity recognition using parallel corpus: A new approach using XLM-RoBERTa alignment. arXiv preprint arXiv:2101.11112. Retrieved from https:\/\/arxiv.org\/abs\/2101.11112"},{"key":"e_1_3_3_36_2","unstructured":"Y. Liu. 2019. RoBERTa: A robustly optimized BERT pretraining approach (1). Available at: http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_3_37_2","article-title":"Decoupled weight decay regularization","author":"Loshchilov I.","year":"2019","unstructured":"I. Loshchilov and F. Hutter. 2019. Decoupled weight decay regularization. 7th International Conference on Learning Representations, ICLR 2019.","journal-title":"7th International Conference on Learning Representations"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104442"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109265"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109894"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.09.017"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU46091.2019.9003958"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331341"},{"key":"e_1_3_3_45_2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","year":"2020","unstructured":"C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and J. L. P. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21, 140 (2020), 1\u201367.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_46_2","first-page":"15","article-title":"Transfer learning in natural language processing tutorial","year":"2019","unstructured":"S. Ruder, M. E. Peters, S. Swayamdipta, and T. Wolf. 2019. Transfer learning in natural language processing tutorial. NAACL HLT 2019\u20132019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\u2013Tutorial Abstracts, (2010), 15\u201318.","journal-title":"NAACL HLT 2019\u20132019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\u2013Tutorial Abstracts"},{"key":"e_1_3_3_47_2","unstructured":"V. S. Sadasivan A. Kumar S. Balasubramanian W. Wang and S. Feizi. 2023. Can AI-generated text be reliably detected?. arXiv preprint arXiv:2303.11156. Retrieved from https:\/\/arxiv.org\/abs\/2303.11156"},{"key":"e_1_3_3_48_2","unstructured":"V. Sanh. 2019. DistilBERT a distilled version of BERT: Smaller faster cheaper and lighter\u2019. 2\u20136. arXiv preprint arXiv:1910.01108. Retrieved from https:\/\/arxiv.org\/abs\/1910.01108"},{"key":"e_1_3_3_49_2","unstructured":"S. Shi E. Zhao D. Tang Y. Wang P. Li W. Bi H. Jiang G. Huang L. Cui X. Huang C. Zhou Y. Dai and D. Ma. 2022. Effidit: Your AI writing assistant\u2019. arXiv preprint arXiv:2208.01815. Retrieved from https:\/\/arxiv.org\/abs\/2208.01815"},{"key":"e_1_3_3_50_2","unstructured":"A. Srinivasan S. Sitaram T. Ganu S. Dandapat K. Bali and M. Choudhury. 2021. Predicting the performance of multilingual NLP models. arXiv preprint arXiv:2110.08875. Retrieved from https:\/\/arxiv.org\/abs\/2110.08875"},{"key":"e_1_3_3_51_2","unstructured":"H. Touvron T. Lavril G. Izacard X. Martinet M. A. Lachaux T. Lacroix B. Rozi\u00e8re N. Goyal E. Hambro F. Azhar A. Rodriguez A. Joulin E. Grave and G. Lample. 2023. LLaMA: Open and efficient foundation language models. Available at: http:\/\/arxiv.org\/abs\/2302.13971"},{"key":"e_1_3_3_52_2","first-page":"1","article-title":"ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content","volume":"3","author":"Uzun L.","year":"2023","unstructured":"L. Uzun. 2023. ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content. Language Education and Technology 3, (2023) 1.","journal-title":"Language Education and Technology"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NIPS'17). 5998\u20136008. 10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"e_1_3_3_54_2","doi-asserted-by":"crossref","unstructured":"V. Verma E. Fleisig N. Tomlin and D. Klein. 2023. Ghostbuster: Detecting text ghostwritten by large language models. 1\u201318. Retrieved from http:\/\/arxiv.org\/abs\/2305.15047","DOI":"10.18653\/v1\/2024.naacl-long.95"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107476"},{"key":"e_1_3_3_56_2","unstructured":"Y. Wang et al. 2023. M4: Multi-generator multi-domain and multi-lingual black-box machine-generated text detection. Available at: http:\/\/arxiv.org\/abs\/2305.14902"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1599"},{"key":"e_1_3_3_58_2","doi-asserted-by":"crossref","unstructured":"D. Weber\u2013Wulf A. Anohina\u2013Naumeca S. Bjelobaba T. Folt\u00fdnek J. Guerrero\u2013Dib O. Popoola P. \u0160igut and L. Waddington. 2023. Testing of detection tools for AI-generated text. International Journal for Educational Integrity 19 1 (2023) 26.","DOI":"10.1007\/s40979-023-00146-z"},{"key":"e_1_3_3_59_2","unstructured":"K. Wu L. Pang H. Shen X. Cheng and T. S. Chua. 2023. LLMDet: A large language models detection tool. arXiv preprint arXiv:2305.15004. Retrieved from https:\/\/arxiv.org\/abs\/2305.15004"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.semeval-1.93"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108586"},{"issue":"21","key":"e_1_3_3_62_2","doi-asserted-by":"crossref","first-page":"23688","DOI":"10.1609\/aaai.v38i21.30527","article-title":"ChatGPT-Generated code assignment detection using perplexity of large language models (student abstract)","volume":"38","author":"Xu Z.","year":"2024","unstructured":"Z. Xu, R. Xu, and V. S. Sheng. 2024. ChatGPT-Generated code assignment detection using perplexity of large language models (student abstract). In Proceedings of the AAAI Conference on Artificial Intelligence 38, 21 (2024), 23688\u201323689).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582262"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543826"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461764"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626523"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3605550"},{"key":"e_1_3_3_68_2","doi-asserted-by":"crossref","unstructured":"Z. Dong J. Ni D. M. Bikel E. Alfonseca Y. Wang C. Qu and I. Zitouni. 2022. Exploring dual encoder architectures for question answering. arXiv:2204.07120. Retrieved from https:\/\/arxiv.org\/abs\/2204.07120","DOI":"10.18653\/v1\/2022.emnlp-main.640"},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","unstructured":"M. Khalilia S. Malaysha R. Suwaileh M. Jarrar A. Aljabari T. Elsayed and I. Zitouni. 2024. ArabicNLU 2024: The first arabic natural language understanding shared task. arXiv:2407.20663. Retrieved from https:\/\/arxiv.org\/abs\/2407.20663","DOI":"10.18653\/v1\/2024.arabicnlp-1.30"},{"key":"e_1_3_3_70_2","article-title":"AI text detection method based on perplexity features with strided sliding window","author":"Liu X.","year":"2024","unstructured":"X. Liu and L. Kong. 2024. AI text detection method based on perplexity features with strided sliding window. Working Notes of Clef.","journal-title":"Working Notes of Clef"},{"key":"e_1_3_3_71_2","unstructured":"T. Kumarage G. Agrawal P. Sheth R. Moraffah A. Chadha J. Garland and H. Liu. 2024. A survey of ai-generated text forensic systems: Detection attribution and characterization. arXiv:2403.01152. Retrieved from https:\/\/arxiv.org\/abs\/2403.01152"},{"key":"e_1_3_3_72_2","unstructured":"Y. Mo H. Qin Y. Dong Z. Zhu and Z. Li. 2024. Large language model (llm) ai text generation detection based on transformer deep learning algorithm. arXiv:2405.06652. Retrieved from https:\/\/arxiv.org\/abs\/2405.06652"},{"key":"e_1_3_3_73_2","doi-asserted-by":"crossref","unstructured":"Z. Lai X. Zhang and S. Chen. 2024. Adaptive ensembles of fine-tuned transformers for llm-generated text detection. arXiv:2403.13335. Retrieved from https:\/\/arxiv.org\/abs\/2403.13335","DOI":"10.1109\/IJCNN60899.2024.10651296"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708889","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708889","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:54Z","timestamp":1750295874000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2,28]]}},"alternative-id":["10.1145\/3708889"],"URL":"https:\/\/doi.org\/10.1145\/3708889","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]},"assertion":[{"value":"2024-09-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}