{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:10:06Z","timestamp":1778364606971,"version":"3.51.4"},"publisher-location":"Cham","reference-count":72,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032190956","type":"print"},{"value":"9783032190963","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-19096-3_16","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:05:39Z","timestamp":1778364339000},"page":"243-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Word Overuse and\u00a0Alignment in\u00a0Large Language Models: The Influence of\u00a0Learning from\u00a0Human Feedback"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3204-3879","authenticated-orcid":false,"given":"Thomas Stephan","family":"Juzek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0160-6656","authenticated-orcid":false,"given":"Zina B.","family":"Ward","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"16_CR1","unstructured":"Koppenburg, P.: Tweet on 01 April 2024. https:\/\/x.com\/PKoppenburg\/status\/1774757167045788010. Accessed 12 Aug 2024"},{"key":"16_CR2","unstructured":"Nguyen, J.: Tweet on 30 March 2024. https:\/\/x.com\/JeremyNguyenPhD\/status\/1774021645709295840. Accessed 12 Aug 2024"},{"key":"16_CR3","unstructured":"Shapira, P.: Delving into \u201cdelve\u201d. https:\/\/pshapira.net\/2024\/03\/31\/delving-into-delve\/. Accessed 21 Sept 2024"},{"key":"16_CR4","unstructured":"Gray, A.: ChatGPT \u201ccontamination\u201d: estimating the prevalence of LLMs in the scholarly literature. arXiv preprint arXiv:2403.16887 (2024)"},{"key":"16_CR5","unstructured":"Kobak, D., Gonz\u00e1lez M\u00e1rquez, R., Horv\u00e1t, E.-\u00c1., Lause, J.: Delving into ChatGPT usage in academic writing through excess vocabulary. arXiv preprint arXiv:2406.07016 (2024)"},{"key":"16_CR6","unstructured":"Liang, W. et al.: Mapping the increasing use of LLMs in scientific papers. arXiv preprint arXiv:2404.01268 (2024)"},{"key":"16_CR7","unstructured":"Liu, J., Bu, Y.: Towards the relationship between AIGC in manuscript writing and author profiles: evidence from preprints in LLMs. arXiv:2404.15799 (2024)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Matsui, K.: Delving into PubMed Records: Some Terms in Medical Writing Have Drastically Changed after the Arrival of ChatGPT. medRxiv (2024)","DOI":"10.1101\/2024.05.14.24307373"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Juzek, T.S., Ward, Z.B.: Why Does ChatGPT \u201cDelve\u201d So Much? exploring the sources of lexical overrepresentation in large language models. In: Proceedings of the 31st International Conference on Computational Linguistics, pp. 6397\u20136411 (2025). https:\/\/doi.org\/10.48550\/arXiv.2412.11385","DOI":"10.48550\/arXiv.2412.11385"},{"key":"16_CR10","unstructured":"Degaetano-Ortlieb, S., Teich, E.: Using relative entropy for detection and analysis of periods of diachronic linguistic change. In: Proceedings of 2nd Joint SIGHUM Workshop, pp. 22\u201333 (2018)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Degaetano-Ortlieb, S., Kermes, H., Khamis, A., Teich, E.: An information-theoretic approach to modeling diachronic change in scientific English. In: From Data to Evidence in English Language Research, pp. 258\u2013281. Brill, Leiden (2018)","DOI":"10.1163\/9789004390652_012"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Bizzoni, Y., Degaetano-Ortlieb, S., Fankhauser, P., Teich, E.: Linguistic variation and change in 250 years of English scientific writing: a data-driven approach. Front. Artif Intell. 3(73) (2020)","DOI":"10.3389\/frai.2020.00073"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Menzel, K.: Medical discourse in Late Modern English: insights from a multidisciplinary corpus of scientific journal articles. In: Corpus Pragmatic Studies on the History of Medical Discourse, pp. 79\u2013104. John Benjamins, Amsterdam (2022)","DOI":"10.1075\/pbns.330.04men"},{"key":"16_CR14","unstructured":"Hern, A.: TechScape: how cheap, outsourced labour in Africa is shaping AI English. https:\/\/www.theguardian.com\/technology\/2024\/apr\/16\/techscape-ai-gadgest-humane-ai-pin-chatgpt. Accessed 12 Aug 2024"},{"key":"16_CR15","unstructured":"Sheikh, H.: Why does ChatGPT use \u201cDelve\u201d so much? Mystery Solved. https:\/\/hesamsheikh.substack.com\/p\/why-does-chatgpt-use-delve-so-much. Accessed 14 Jan 2025"},{"key":"16_CR16","unstructured":"Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences. In: Advance in Neural Information Processing System, vol. 30 (2017)"},{"key":"16_CR17","unstructured":"Ziegler, D.M., et al.: Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019)"},{"key":"16_CR18","unstructured":"Rafailov, R. et al.: Direct preference optimization: your language model is secretly a reward model. In: Advance in Neural Information Processing System, vol. 36 (2024)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"He, Z., Guo, S., Rao, A., Lerman, K.: Whose emotions and moral sentiments do language models reflect? arXiv preprint arXiv:2402.11114 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.395"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610\u2013623 (2021)","DOI":"10.1145\/3442188.3445922"},{"key":"16_CR21","unstructured":"Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., Hashimoto, T.: Whose opinions do language models reflect? In: International Conference on Machine Learning (ICML), pp. 29971\u201330004 (2023)"},{"key":"16_CR22","unstructured":"Durmus, E. et al.: Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388 (2023)"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Ouyang, L. et al.: Training language models to follow instructions with human feedback. In: Advance in Neural Information Processing System, vol. 35, pp. 27730\u201327744 (2022)","DOI":"10.52202\/068431-2011"},{"key":"16_CR24","unstructured":"Bommasani, R. et al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Geng, M., Chen, C., Wu, Y., Chen, D., Wan, Y., Zhou, P.: The impact of large language models in academia: from writing to speaking. arXiv preprint arXiv:2409.13686 (2024)","DOI":"10.18653\/v1\/2025.findings-acl.987"},{"key":"16_CR26","unstructured":"Yakura, H., Lopez-Lopez, E., Brinkmann, L., Serna, I., Gupta, P., Rahwan, I.: Empirical evidence of large language model\u2019s influence on human spoken communication. arXiv preprint arXiv:2409.01754 (2024)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Zamaraeva, O., Flickinger, D., Bond, F., G\u00f3mez-Rodr\u00edguez, C.: Comparing LLM-generated and human-authored news text using formal syntactic theory. arXiv preprint arXiv:2506.01407 (2025)","DOI":"10.18653\/v1\/2025.acl-long.443"},{"key":"16_CR28","unstructured":"Sculley, D. et al.: Hidden technical debt in machine learning systems. In: Advance in Neural Information Processing System, vol. 28 (2015)"},{"issue":"2","key":"16_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3639372","volume":"15","author":"H Zhao","year":"2024","unstructured":"Zhao, H., et al.: Explainability for large language models: a survey. ACM Trans. Intell. Syst. Technol. 15(2), 1\u201338 (2024)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"16_CR30","unstructured":"Cambria, E. et al.: XAI meets LLMs: a survey of the relation between explainable AI and large language models. arXiv preprint arXiv:2407.15248 (2024)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xiong, W., Chen, L., Zhou, T., Huang, H., Zhang, T.: From lists to emojis: how format bias affects model alignment. arXiv:2409.11704 (2024)","DOI":"10.18653\/v1\/2025.acl-long.1308"},{"key":"16_CR32","unstructured":"Dubey, A. et al.: The LLaMA 3 herd of models. arXiv:2407.21783 (2024)"},{"key":"16_CR33","unstructured":"Wolf, T. et al.: Transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2020)"},{"key":"16_CR34","unstructured":"Hugging Face Team: Open LLM Leaderboard (2024). https:\/\/huggingface.co\/spaces\/open-llm-leaderboard\/open_llm_leaderboard"},{"key":"16_CR35","unstructured":"Allen Institute for AI (2024). OLMo 2. https:\/\/allenai.org\/olmo"},{"key":"16_CR36","unstructured":"Technology Innov. Inst. (2024). Falcon 3. https:\/\/falconllm.tii.ae\/falcon3\/index.html"},{"key":"16_CR37","unstructured":"Juzek, T.S., Ward, Z.B.: Supplementary materials for \u201cWord overuse and alignment in large language models: The influence of learning from human feedback\". OSF (2025). https:\/\/osf.io\/4nvjkhttps:\/\/doi.org\/10.17605\/OSF.IO\/4NVJK"},{"key":"16_CR38","unstructured":"Python Software Foundation: Python 3. https:\/\/www.python.org\/. Accessed 2024"},{"key":"16_CR39","unstructured":"National Library of Medicine: PubMed Database. https:\/\/pubmed.ncbi.nlm.nih.gov\/. Accessed 24 Nov 2024"},{"key":"16_CR40","unstructured":"Achiam, J. et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"16_CR41","unstructured":"OpenAI: OpenAI Python API. Version 1.57. https:\/\/platform.openai.com\/docs\/. Accessed 18 Jan 2025"},{"key":"16_CR42","doi-asserted-by":"publisher","unstructured":"Montani, I., Honnibal, M., Boyd, A., Van Landeghem, S., Peters, H.: explosion\/spaCy: v3.7.2: Fixes for APIs and requirements. Zenodo. (2023). https:\/\/doi.org\/10.5281\/zenodo.10009823","DOI":"10.5281\/zenodo.10009823"},{"key":"16_CR43","unstructured":"Google: Google Books Ngram Viewer. https:\/\/books.google.com\/ngrams\/. Accessed 02 Jan 2025"},{"issue":"4","key":"16_CR44","first-page":"27","volume":"8","author":"T Lavergne","year":"2008","unstructured":"Lavergne, T., Urvoy, T., Yvon, F.: Detecting fake content with relative entropy scoring. Pan 8(4), 27\u201331 (2008)","journal-title":"Pan"},{"key":"16_CR45","unstructured":"Chakraborty, S. et al.: On the possibilities of AI-generated text detection. arXiv preprint arXiv:2304.04736 (2023)"},{"key":"16_CR46","unstructured":"Mitchell, E., Lee, Y., Khazatsky, A., Manning, C.D., Finn, C.: DetectGPT: zero-shot machine-generated text detection using probability curvature. In: International Conference on Machine Learning (ICML), pp. 24950\u201324962 (2023)"},{"key":"16_CR47","unstructured":"Huang, Y. et al.: MAGRET: machine-generated text detection with rewritten texts. In: Proceedings of COLING 2025, pp. 8336\u20138346 (2025)"},{"key":"16_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28316-6","volume-title":"An Introduction to Statistics with Python","author":"T Haslwanter","year":"2016","unstructured":"Haslwanter, T.: An Introduction to Statistics with Python. Springer, Cham (2016)"},{"issue":"4","key":"16_CR49","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0306396818823172","volume":"60","author":"M Kwet","year":"2019","unstructured":"Kwet, M.: Digital colonialism: US empire and the new imperialism in the global south. Race Class 60(4), 3\u201326 (2019)","journal-title":"Race Class"},{"key":"16_CR50","unstructured":"Perrigo, B.: Exclusive: OpenAI used Kenyan workers on less than \\$2 per hour to make ChatGPT less toxic. Time Mag. 18 (2023)"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Rohde, F. et al.: Broadening the perspective for sustainable artificial intelligence: sustainability criteria and indicators for artificial intelligence systems. Curr. Opin. Environ. Sustain. 66, 101411 (2024)","DOI":"10.1016\/j.cosust.2023.101411"},{"key":"16_CR52","volume-title":"Experimental Syntax","author":"W Cowart","year":"1997","unstructured":"Cowart, W.: Experimental Syntax. Sage, Thousand Oaks (1997)"},{"issue":"3","key":"16_CR53","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1111\/ajps.12081","volume":"58","author":"AJ Berinsky","year":"2014","unstructured":"Berinsky, A.J., Margolis, M.F., Sances, M.W.: Separating the shirkers from the workers? Making sure respondents pay attention on self-administered surveys. Am. J. Polit. Sci. 58(3), 739\u2013753 (2014)","journal-title":"Am. J. Polit. Sci."},{"key":"16_CR54","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.jrp.2013.09.008","volume":"48","author":"MR Maniaci","year":"2014","unstructured":"Maniaci, M.R., Rogge, R.D.: Caring about carelessness: participant inattention and its effects on research. J. Res. Pers. 48, 61\u201383 (2014)","journal-title":"J. Res. Pers."},{"key":"16_CR55","unstructured":"Friedman, H.H., Herskovitz, P.J., Pollack, S.: The biasing effects of scale-checking styles on response to a Likert scale. In: Proceedings of American Statistics Association Confeence on Survey Research Methods, vol. 792, pp. 792\u2013795 (1994)"},{"issue":"9","key":"16_CR56","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1002\/pfi.21800","volume":"57","author":"SY Chyung","year":"2018","unstructured":"Chyung, S.Y., Kennedy, M., Campbell, I.: Evidence-based survey design: the use of ascending or descending order of Likert-type response options. Perform. Improv. 57(9), 9\u201316 (2018)","journal-title":"Perform. Improv."},{"issue":"3","key":"16_CR57","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1353\/lan.2016.0052","volume":"92","author":"K Mahowald","year":"2016","unstructured":"Mahowald, K., Graff, P., Hartman, J., Gibson, E.: SNAP judgments: a small N acceptability paradigm (SNAP) for linguistic acceptability judgments. Language 92(3), 619\u2013635 (2016)","journal-title":"Language"},{"key":"16_CR58","doi-asserted-by":"publisher","unstructured":"H\u00e4ussler, J., Juzek, T.: Hot topics surrounding acceptability judgement tasks. In: Featherston, S., H\u00f6rnig, R., Steinberg, R., Umbreit, B., Wallis, J. (eds.) Linguistic Evidence 2016: Empirical, Theoretical, and Computational Perspectives. University of T\u00fcbingen, T\u00fcbingen (2017). https:\/\/doi.org\/10.15496\/publikation-19039","DOI":"10.15496\/publikation-19039"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Downs, J.S., Holbrook, M.B., Sheng, S., Cranor, L.F.: Are your participants gaming the system? Screening Mechanical Turk workers. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 2399\u20132402 (2010)","DOI":"10.1145\/1753326.1753688"},{"key":"16_CR60","unstructured":"Zhu, D., Carterette, B.: An analysis of assessor behavior in crowdsourced preference judgments. In: SIGIR 2010 Workshop on Crowdsourcing for Search Evaluation, pp. 17\u201320 (2010)"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Kazai, G., Kamps, J., Milic-Frayling, N.: Worker types and personality traits in crowdsourcing relevance labels. In: Proceedings of 20th ACM International Conference on Information and Knowledge Management, pp. 1941\u20131944 (2011)","DOI":"10.1145\/2063576.2063860"},{"key":"16_CR62","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.chb.2017.08.038","volume":"77","author":"KA Thomas","year":"2017","unstructured":"Thomas, K.A., Clifford, S.: Validity and mechanical turk: an assessment of exclusion methods and interactive experiments. Comp. Hum. B. 77, 184\u2013197 (2017)","journal-title":"Comp. Hum. B."},{"issue":"1","key":"16_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3148148","volume":"51","author":"F Daniel","year":"2018","unstructured":"Daniel, F., Kucherbaev, P., Cappiello, C., Benatallah, B., Allahbakhsh, M.: Quality control in crowdsourcing: a survey of quality attributes, assessment techniques, and assurance actions. ACM Comput. Surv. 51(1), 1\u201340 (2018)","journal-title":"ACM Comput. Surv."},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Labov, W.: Principles of Linguistic Change, vol. 3: Cognitive and Cultural Factors. Wiley, Hoboken (2011)","DOI":"10.1002\/9781444327496"},{"key":"16_CR65","doi-asserted-by":"crossref","unstructured":"Young, J., et al.: The role of AI in peer support for young people: a study of preferences for human-and AI-generated responses. In: Proceedings of CHI Conference on Human Factors in Computing Systems, pp. 1\u201318 (2024)","DOI":"10.1145\/3613904.3642574"},{"key":"16_CR66","unstructured":"Wu, M., Aji, A.F.: Style over substance: evaluation biases for large language models. In Proceedings COLING 2025, pp. 297\u2013312. Association for Computational Linguistics, Abu Dhabi, UAE (2025). https:\/\/aclanthology.org\/2025.coling-main.21\/"},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"hMensa, P.A.: Artificial intelligence and the future of sociolinguistic research: an African contextual review. J. Socioling. (2024)","DOI":"10.1111\/josl.12679"},{"key":"16_CR68","unstructured":"Templeton, A.: Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet. Anthropic (2024)"},{"key":"16_CR69","doi-asserted-by":"crossref","unstructured":"Toxtli, C., Suri, S., Savage, S.: Quantifying the invisible labor in crowd work. Proc. ACM Hum.-Comput. Interact. 5(CSCW2), 1\u201326 (2021)","DOI":"10.1145\/3476060"},{"key":"16_CR70","unstructured":"Roberts, J.: The Precarious Human Work Behind AI. https:\/\/www.accel.ai\/anthology\/2023\/5\/22\/jyzu7sbpzyxufu5l1ekidxj0g7jafh Accessed 2023"},{"key":"16_CR71","unstructured":"Novick, M.: A.I\u2019.s Dirty Secret: It\u2019s Powered by Digital Sweatshops. https:\/\/change-links.org\/a-i-s-dirty-secret-its-powered-by-digital-sweatshops\/. Accessed 2023"},{"key":"16_CR72","unstructured":"Jim the AI Whisperer: How One Sentence Pattern Can Expose AI Writing. Medium. https:\/\/generativeai.pub\/how-to-spot-ai-writing-with-one-sentence-pattern-8aa5b3ec5a63. Accessed 2024\/12"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19096-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:05:53Z","timestamp":1778364353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19096-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032190956","9783032190963"],"references-count":72,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19096-3_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}