{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:34:24Z","timestamp":1760488464050,"version":"build-2065373602"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oper. Res. Forum"],"DOI":"10.1007\/s43069-025-00486-1","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T10:09:53Z","timestamp":1749031793000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classifying AI vs. Human Content: Integrating BERT and Linguistic Features for Enhanced Classification"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6293-0805","authenticated-orcid":false,"given":"Abhishek","family":"Yadav","sequence":"first","affiliation":[]},{"given":"Shunmuga Priya","family":"MC","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"486_CR1","unstructured":"Kirchenbauer, J, Geiping, J, Wen, Y, Katz, J, Miers, I, Goldstein T (2023) A watermark for large language models. In: International Conference on Machine Learning. PMLR, pp 17061\u201317084"},{"key":"486_CR2","doi-asserted-by":"publisher","unstructured":"Munyer T, Tanvir A, Das A, Zhong X (2024) DeepTextMark: a deep learning-driven text watermarking approach for identifying large language model generated text. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3376693","DOI":"10.1109\/ACCESS.2024.3376693"},{"key":"486_CR3","unstructured":"Yang X, Chen K, Zhang W, Liu C, Qi Y, Zhang J, Fang H, Yu N (2023) Watermarking text generated by black-box language models. arXiv preprint  arXiv:2305.08883"},{"key":"486_CR4","unstructured":"Zhao X, Ananth P, Li L, Wang YX (2023) Provable robust watermarking for AI-generated text. arXiv preprint  arXiv:2306.17439"},{"key":"486_CR5","unstructured":"Kumarage, T, Garland, J, Bhattacharjee, A, Trapeznikov, K, Ruston, S, Liu H (2023) Stylometric detection ofAI-generated text in twitter timelines. arXiv preprint  arXiv:2303.03697"},{"key":"486_CR6","unstructured":"Mikros G, Koursaris A, Bilianos D, Markopoulos G (2023) AI-writing detection using an ensemble of transformers and stylometric features. In: CEUR Workshop Proceedings, vol 3496. CEUR-WS, Aachen"},{"key":"486_CR7","unstructured":"Nguyen-Son HQ, Tieu NDT, Nguyen HH, Yamagishi J, Zen IE (2017) Identifying computer-generated text using statistical analysis. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, pp 1504\u20131511"},{"key":"486_CR8","unstructured":"Wang Q, Zhang L, Guo Z, Mao Z (2024) IDEATE:detecting AI-generated text using internal and external factual structures. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) pp 8556\u20138568"},{"key":"486_CR9","unstructured":"Zhong W, Tang D, Xu Z, Wang R, Duan N, Zhou M, Yin J (2020) Neural deepfake detection with factual structure of text. arXiv preprint  arXiv:2010.07475"},{"key":"486_CR10","unstructured":"Shah A, Ranka P, Dedhia U, Prasad S, Muni S, Bhowmick K (2023) Detecting andunmasking AI-generated texts through explainable artificial intelligence using stylistic features. Int J Adv Comput Sci Appl 14(10):110"},{"key":"486_CR11","doi-asserted-by":"publisher","first-page":"e443","DOI":"10.7717\/peerj-cs.443","volume":"7","author":"L Fr\u00f6hling","year":"2021","unstructured":"Fr\u00f6hling L, Zubiaga A (2021) Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover. PeerJ Comput Sci 7:e443","journal-title":"PeerJ Comput Sci"},{"key":"486_CR12","unstructured":"Prova N (2024) Detecting AI generated text based on NLP and machine learning approaches. arXiv preprint  arXiv:2404.10032"},{"key":"486_CR13","unstructured":"Sanchez-Medina JJ (2024) Sentiment analysis and random forest to classify LLM versus human source applied to scientific texts. arXiv preprint  arXiv:2404.08673"},{"key":"486_CR14","doi-asserted-by":"publisher","unstructured":"Chae Y, Davidson T (2023) Large language models for text classification: from zero-shot learning to\u00a0instruction-tuning.\u00a0Sociol Methods Res. https:\/\/doi.org\/10.1177\/00491241251325243","DOI":"10.1177\/00491241251325243"},{"issue":"5","key":"486_CR15","doi-asserted-by":"publisher","first-page":"e0251415","DOI":"10.1371\/journal.pone.0251415","volume":"16","author":"T Fagni","year":"2021","unstructured":"Fagni T, Falchi F, Gambini M, Martella A, Tesconi M (2021) TweepFake: about detecting deepfake tweets. PLoS One 16(5):e0251415","journal-title":"PLoS One"},{"issue":"1","key":"486_CR16","first-page":"3498123","volume":"2022","author":"R Qasim","year":"2022","unstructured":"Qasim R, Bangyal WH, Alqarni MA, Ali Almazroi A (2022) A fine-tuned BERT-based transfer learning approach for text classification. Journal of healthcare engineering 2022(1):3498123","journal-title":"Journal of healthcare engineering"},{"key":"486_CR17","unstructured":"Li Y, Li Q, Cui L, Bi W, Wang L, Yang L, Zhang Y (2023) Deepfake text detection in the wild. arXiv preprint  arXiv:2305.13242"}],"container-title":["Operations Research Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43069-025-00486-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43069-025-00486-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43069-025-00486-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T15:58:05Z","timestamp":1760457485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43069-025-00486-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,4]]},"references-count":17,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["486"],"URL":"https:\/\/doi.org\/10.1007\/s43069-025-00486-1","relation":{},"ISSN":["2662-2556"],"issn-type":[{"type":"electronic","value":"2662-2556"}],"subject":[],"published":{"date-parts":[[2025,6,4]]},"assertion":[{"value":"17 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"77"}}