{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:57:12Z","timestamp":1773482232278,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"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":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s41060-025-00936-3","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T11:43:51Z","timestamp":1764416631000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Public opinion and emotional discourse: a study of YouTube comments on Mexican news in 2024"],"prefix":"10.1007","volume":"21","author":[{"given":"Miryam Elizabeth","family":"Villa-P\u00e9rez","sequence":"first","affiliation":[]},{"given":"Ra\u00fal","family":"Monroy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"936_CR1","unstructured":"DataReportal, M., We Are\u00a0Social: Leading countries based on YouTube audience size as of February 2025 (in millions). https:\/\/www.statista.com\/statistics\/280685\/number-of-monthly-unique-youtube-users\/. Accessed: 2025-5-23"},{"key":"936_CR2","unstructured":"Kemp, S.: Digital 2024: Mexico. https:\/\/datareportal.com\/reports\/digital-2024-mexico. Accessed: 2025-8-15"},{"key":"936_CR3","unstructured":"AMIPCI: Reach of leading social networks in Mexico as of May 2024. https:\/\/www.statista.com\/statistics\/449869\/mexico-social-network-penetration\/. Accessed: 2025-5-23"},{"key":"936_CR4","unstructured":"DataReportal, M. &\u00a0We Are\u00a0Social: Average monthly time spent on YouTube app in selected Latin American countries in November 2024 (in hours.minutes). https:\/\/www.statista.com\/statistics\/1407116\/youtube-use-month-latam-countries\/. Accessed: 2025-5-23"},{"key":"936_CR5","unstructured":"Journalism, R.I.: Leading social networks used weekly for news in Mexico as of February 2024. https:\/\/www.statista.com\/statistics\/981906\/social-media-platforms-used-weekly-news-mexico\/. Accessed: 2025-5-23"},{"issue":"1","key":"936_CR6","doi-asserted-by":"publisher","first-page":"7283166","DOI":"10.1155\/2023\/7283166","volume":"2023","author":"SL Evans","year":"2023","unstructured":"Evans, S.L., Jones, R., Alkan, E., Sichman, J.S.A., Haque, A., Oliveira, F.B.S., Mougouei, D.: The emotional impact of covid-19 news reporting: a longitudinal study using natural language processing. Hum. Behav. Emerg. Technol. 2023(1), 7283166 (2023). https:\/\/doi.org\/10.1155\/2023\/7283166","journal-title":"Hum. Behav. Emerg. Technol."},{"key":"936_CR7","doi-asserted-by":"publisher","first-page":"16883","DOI":"10.1109\/ACCESS.2022.3150329","volume":"10","author":"FB Oliveira","year":"2022","unstructured":"Oliveira, F.B., Haque, A., Mougouei, D., Evans, S., Sichman, J.S.A., Singh, M.P.: Investigating the emotional response to covid-19 news on twitter: a topic modeling and emotion classification approach. IEEE Access 10, 16883\u201316897 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3150329","journal-title":"IEEE Access"},{"key":"936_CR8","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.puhe.2023.02.018","volume":"218","author":"VS Anoop","year":"2023","unstructured":"Anoop, V.S., Sreelakshmi, S.: Public discourse and sentiment during mpox outbreak: an analysis using natural language processing. Public Health 218, 114\u2013120 (2023). https:\/\/doi.org\/10.1016\/j.puhe.2023.02.018","journal-title":"Public Health"},{"key":"936_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/geosciences15030100","author":"D Erokhin","year":"2025","unstructured":"Erokhin, D.: Public discourse surrounding the 2025 California wildfires: a sentiment and topic analysis of high-engagement YouTube comments. Geosciences (2025). https:\/\/doi.org\/10.3390\/geosciences15030100","journal-title":"Geosciences"},{"key":"936_CR10","doi-asserted-by":"publisher","unstructured":"Chen, Y., Sack, H., Alam, M.: Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration. Soc. Netw. Anal. Min. 12(1), 135 (2022). https:\/\/doi.org\/10.1007\/s13278-022-00915-7","DOI":"10.1007\/s13278-022-00915-7"},{"key":"936_CR11","doi-asserted-by":"publisher","unstructured":"Latorre, J.P., Amores, J.J.: Topic modelling of racist and xenophobic youtube comments. analyzing hate speech against migrants and refugees spread through youtube in spanish. In: Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM\u201921). TEEM\u201921, pp. 456\u2013460. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3486011.3486494","DOI":"10.1145\/3486011.3486494"},{"issue":"2","key":"936_CR12","doi-asserted-by":"publisher","first-page":"215824402412564","DOI":"10.1177\/21582440241256438","volume":"14","author":"R Mall","year":"2024","unstructured":"Mall, R., Nagpal, M., Salminen, J., Almerekhi, H., Jung, S.-G., Jansen, B.J.: Politics on YouTube: detecting online group polarization based on news videos\u2019 comments. SAGE Open 14(2), 21582440241256440 (2024). https:\/\/doi.org\/10.1177\/21582440241256438","journal-title":"SAGE Open"},{"key":"936_CR13","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez-G\u00e1lvez, A.M., \u00c1lvarez-Gonz\u00e1lez, R., Molina-Iturbide, S.A., Albores-Velasco, F.J.: Exploring political polarization in M\u00e9xico: automatic classification of comments on YouTube. Computaci\u00f3n y Sistemas 28(4), 2231\u20132242 (2024). https:\/\/doi.org\/10.13053\/cys-28-4-5295","DOI":"10.13053\/cys-28-4-5295"},{"issue":"6245","key":"936_CR14","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1126\/science.aaa8685","volume":"349","author":"J Hirschberg","year":"2015","unstructured":"Hirschberg, J., Manning, C.D.: Advances in natural language processing. Science 349(6245), 261\u2013266 (2015). https:\/\/doi.org\/10.1126\/science.aaa8685","journal-title":"Science"},{"key":"936_CR15","doi-asserted-by":"publisher","unstructured":"Fanni, S.C., Febi, M., Aghakhanyan, G., Neri, E.: Natural language processing. In: Klontzas, M.E., Fanni, S.C., Neri, E. (eds.) Natural Language Processing, pp. 87\u201399. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25928-9_5","DOI":"10.1007\/978-3-031-25928-9_5"},{"issue":"1","key":"936_CR16","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s13278-021-00776-6","volume":"11","author":"P Nandwani","year":"2021","unstructured":"Nandwani, P., Verma, R.: A review on sentiment analysis and emotion detection from text. Soc. Netw. Anal. Min. 11(1), 81 (2021). https:\/\/doi.org\/10.1007\/s13278-021-00776-6","journal-title":"Soc. Netw. Anal. Min."},{"key":"936_CR17","unstructured":"Ekman, P.: Emotion in the Human Face., Second edition \/ edited by paul ekman. edn. Studies in emotion and social interaction. Cambridge University Press, Cambridge (1982)"},{"issue":"4\u20135","key":"936_CR18","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1177\/053901882021004003","volume":"21","author":"R Plutchik","year":"1982","unstructured":"Plutchik, R.: A psychoevolutionary theory of emotions. Soc. Sci. Inf. 21(4\u20135), 529\u2013553 (1982). https:\/\/doi.org\/10.1177\/053901882021004003","journal-title":"Soc. Sci. Inf."},{"key":"936_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126232","volume":"546","author":"MS Jahan","year":"2023","unstructured":"Jahan, M.S., Oussalah, M.: A systematic review of hate speech automatic detection using natural language processing. Neurocomputing 546, 126232 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.126232","journal-title":"Neurocomputing"},{"key":"936_CR20","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.eswa.2017.07.040","volume":"89","author":"HJ Escalante","year":"2017","unstructured":"Escalante, H.J., Villatoro-Tello, E., Garza, S.E., L\u00f3pez-Monroy, A.P., Montes-y-G\u00f3mez, M., Villase\u00f1or-Pineda, L.: Early detection of deception and aggressiveness using profile-based representations. Expert Syst. Appl. 89, 99\u2013111 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2017.07.040","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"936_CR21","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., Zhao, L.: Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed. Tools Appl. 78(11), 15169\u201315211 (2019). https:\/\/doi.org\/10.1007\/s11042-018-6894-4","journal-title":"Multimed. Tools Appl."},{"key":"936_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.140233","volume":"434","author":"YP Mulyani","year":"2024","unstructured":"Mulyani, Y.P., Saifurrahman, A., Arini, H.M., Rizqiawan, A., Hartono, B., Utomo, D.S., Spanellis, A., Beltran, M., Banjar Nahor, K.M., Paramita, D., Harefa, W.D.: Analyzing public discourse on photovoltaic (pv) adoption in Indonesia: a topic-based sentiment analysis of news articles and social media. J. Clean. Prod. 434, 140233 (2024). https:\/\/doi.org\/10.1016\/j.jclepro.2023.140233","journal-title":"J. Clean. Prod."},{"key":"936_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2023.103734","volume":"92","author":"AM Shah","year":"2023","unstructured":"Shah, A.M., Schweiggart, N.: #boycottmurree campaign on twitter: Monitoring public response to the negative destination events during a crisis. Int. J. Disast. Risk Reduct. 92, 103734 (2023). https:\/\/doi.org\/10.1016\/j.ijdrr.2023.103734","journal-title":"Int. J. Disast. Risk Reduct."},{"issue":"5","key":"936_CR24","doi-asserted-by":"publisher","first-page":"128","DOI":"10.2196\/jmir.3863","volume":"17","author":"L Mollema","year":"2015","unstructured":"Mollema, L., Harmsen, I.A., Broekhuizen, E., Clijnk, R., De Melker, H., Paulussen, T., Kok, G., Ruiter, R., Das, E.: Disease detection or public opinion reflection? content analysis of tweets, other social media, and online newspapers during the measles outbreak in the netherlands in 2013. J. Med. Internet Res. 17(5), 128 (2015). https:\/\/doi.org\/10.2196\/jmir.3863","journal-title":"J. Med. Internet Res."},{"key":"936_CR25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.tbs.2020.05.005","volume":"21","author":"B Qi","year":"2020","unstructured":"Qi, B., Costin, A., Jia, M.: A framework with efficient extraction and analysis of twitter data for evaluating public opinions on transportation services. Travel Behav. Soc. 21, 10\u201323 (2020). https:\/\/doi.org\/10.1016\/j.tbs.2020.05.005","journal-title":"Travel Behav. Soc."},{"key":"936_CR26","doi-asserted-by":"publisher","unstructured":"Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. Proc. Int. AAAI Conf. Web Soc. Media 8(1), 216\u2013225 (2014). https:\/\/doi.org\/10.1609\/icwsm.v8i1.14550","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"936_CR27","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"936_CR28","doi-asserted-by":"publisher","first-page":"47826","DOI":"10.2196\/47826","volume":"26","author":"A Molenaar","year":"2024","unstructured":"Molenaar, A., Lukose, D., Brennan, L., Jenkins, E.L., McCaffrey, T.A.: Using natural language processing to explore social media opinions on food security: sentiment analysis and topic modeling study. J. Med. Internet Res. 26, 47826 (2024). https:\/\/doi.org\/10.2196\/47826","journal-title":"J. Med. Internet Res."},{"key":"936_CR29","doi-asserted-by":"publisher","first-page":"64838","DOI":"10.2196\/64838","volume":"27","author":"HJ Hwang","year":"2025","unstructured":"Hwang, H.J., Kim, N., You, J.Y., Ryu, H.R., Kim, S.-Y., Yoon Park, J.H., Lee, K.W.: Harnessing social media data to understand information needs about kidney diseases and emotional experiences with disease management: Topic and sentiment analysis. J. Med. Internet Res. 27, 64838 (2025). https:\/\/doi.org\/10.2196\/64838","journal-title":"J. Med. Internet Res."},{"key":"936_CR30","doi-asserted-by":"publisher","first-page":"47508","DOI":"10.2196\/47508","volume":"26","author":"F Guo","year":"2024","unstructured":"Guo, F., Liu, Z., Lu, Q., Ji, S., Zhang, C.: Public opinion about covid-19 on a microblog platform in China: topic modeling and multidimensional sentiment analysis of social media. J. Med. Internet Res. 26, 47508 (2024). https:\/\/doi.org\/10.2196\/47508","journal-title":"J. Med. Internet Res."},{"key":"936_CR31","doi-asserted-by":"publisher","first-page":"53434","DOI":"10.2196\/53434","volume":"5","author":"F Alshanik","year":"2025","unstructured":"Alshanik, F., Khasawneh, R., Dalky, A., Qawasmeh, E.: Unveiling topics and emotions in Arabic tweets surrounding the covid-19 pandemic: topic modeling and sentiment analysis approach. JMIR Infodemiol. 5, 53434 (2025). https:\/\/doi.org\/10.2196\/53434","journal-title":"JMIR Infodemiol."},{"issue":"1","key":"936_CR32","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1140\/epjds\/s13688-024-00501-1","volume":"13","author":"F Pierri","year":"2024","unstructured":"Pierri, F.: Drivers of hate speech in political conversations on twitter: the case of the 2022 Italian general election. EPJ Data Sci. 13(1), 63 (2024). https:\/\/doi.org\/10.1140\/epjds\/s13688-024-00501-1","journal-title":"EPJ Data Sci."},{"key":"936_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2023.109938","volume":"52","author":"LH Chowdhury","year":"2024","unstructured":"Chowdhury, L.H., Islam, S., Shatabda, S.: A Bengali news and public opinion dataset from YouTube. Data Brief 52, 109938 (2024). https:\/\/doi.org\/10.1016\/j.dib.2023.109938","journal-title":"Data Brief"},{"issue":"2","key":"936_CR34","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1177\/0739532915587291","volume":"36","author":"PS Chen","year":"2015","unstructured":"Chen, P.S., Wilson, N., Chen, G.M., Chang, C.-W.: Longer, higher quality videos preferred by news viewers. Newsp. Res. J. 36(2), 212\u2013224 (2015). https:\/\/doi.org\/10.1177\/0739532915587291","journal-title":"Newsp. Res. J."},{"key":"936_CR35","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, J.M., Giudici, J.C., Luque, F.: pysentimiento: A Python toolkit for sentiment analysis and SocialNLP tasks (2023)","DOI":"10.21203\/rs.3.rs-3570648\/v1"},{"key":"936_CR36","first-page":"163","volume":"2664","author":"M Garci\u00e1-Vegaa","year":"2020","unstructured":"Garci\u00e1-Vegaa, M., Di\u00e1z-Galiano, M.C., Garci\u00e1-Cumbreras, M., Del Arco, F., Montejo-Ra\u00e9z, A., Jim\u00e9nez-Zafra, S., C\u00e1mara, E., Aguilar, C., Cabezudo, M., Chiruzzo, L., Moctezuma, D.: Overview of TASS 2020: introducing emotion detection. CEUR Workshop Proceedings 2664, 163\u2013170 (2020)","journal-title":"CEUR Workshop Proceedings"},{"issue":"1","key":"936_CR37","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37\u201346 (1960). https:\/\/doi.org\/10.1177\/001316446002000104","journal-title":"Educ. Psychol. Measur."},{"key":"936_CR38","doi-asserted-by":"publisher","unstructured":"Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Rangel\u00a0Pardo, F.M., Rosso, P., Sanguinetti, M.: Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In: May, J., Shutova, E., Herbelot, A., Zhu, X., Apidianaki, M., Mohammad, S.M. (eds.) Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA, pp. 54\u201363 (2019). https:\/\/doi.org\/10.18653\/v1\/S19-2007","DOI":"10.18653\/v1\/S19-2007"},{"key":"936_CR39","unstructured":"Grootendorst, M.: BERTopic: Neural topic modeling with a class-based TF-IDF procedure. Preprint at https:\/\/arxiv.org\/abs\/2203.05794 (2022)"},{"key":"936_CR40","unstructured":"editorial, P.: The Best Days to Distribute Press Releases for Maximum Impact. https:\/\/presscloud.ai\/en\/pr-academy\/the-best-days-to-distribute-press-releases-for-maximum-impact-1. Accessed: 2025-5-12"},{"key":"936_CR41","unstructured":"Social, S.: Best times to post on social media in 2025. https:\/\/sproutsocial.com\/insights\/best-times-to-post-on-social-media\/. Accessed: 2025-5-12"},{"key":"936_CR42","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262019835.001.0001","volume-title":"The News Gap: When the Information Preferences of the Media and the Public Diverge","author":"PJ Boczkowski","year":"2013","unstructured":"Boczkowski, P.J., Mitchelstein, E.: The News Gap: When the Information Preferences of the Media and the Public Diverge. MIT Press, Cambridge (2013)"},{"key":"936_CR43","unstructured":"Castillo-Camarena, C.: Judicial Reform in Mexico: a Comparative Between the Old and the New Process for Electing Judges. https:\/\/www.wilsoncenter.org\/article\/judicial-reform-mexico-comparative-between-old-and-new-process-electing-judges. Accessed: 2025-5-15"},{"key":"936_CR44","unstructured":"Abu-Manneh, R., L\u00f3pez\u00a0Ortiz, A., Aboim, L., Caicedo, J., Scheffer Da Silveira, G., Fernandes De Andrade, G., Mata-Morreo, G., Weiss, D.H., Salinas, G.J., Lacreta, I., Portillo-Diaz, M., Dubot, L., Tablada-Flores, J.P.: Mexico\u2019s controversial judicial reform takes effect: Assessing its impact. Technical report, Mayer Brown (oct 2024). https:\/\/www.mayerbrown.com\/en\/insights\/publications\/2024\/10\/mexicos-controversial-judicial-reform-takes-effect-assessing-its-impact"},{"key":"936_CR45","unstructured":"Mexico, U.S.M.: On Mexico\u2019s Judicial Reform Proposal. https:\/\/mx.usembassy.gov\/on-mexicos-judicial-reform-proposal\/. Accessed: 2025-5-15"},{"key":"936_CR46","unstructured":"Brewer, S.: udicial Reform in Mexico: A Setback for Human Rights. https:\/\/www.wola.org\/analysis\/judicial-reform-in-mexico-a-setback-for-human-rights\/. Accessed: 2025-5-15"},{"issue":"1","key":"936_CR47","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1080\/17482798.2021.1915831","volume":"16","author":"H Lowenstein-Barkai","year":"2022","unstructured":"Lowenstein-Barkai, H., Lev-on, A.: News videos consumption in an age of new media: a comparison between adolescents and adults. J. Child. Media 16(1), 78\u201394 (2022). https:\/\/doi.org\/10.1080\/17482798.2021.1915831","journal-title":"J. Child. Media"},{"issue":"4","key":"936_CR48","doi-asserted-by":"publisher","first-page":"205630512211302","DOI":"10.1177\/20563051221130282","volume":"8","author":"O Tenenboim","year":"2022","unstructured":"Tenenboim, O.: Comments, shares, or likes: What makes news posts engaging in different ways. Soc. Med. Soc. 8(4), 20563051221130280 (2022). https:\/\/doi.org\/10.1177\/20563051221130282","journal-title":"Soc. Med. Soc."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00936-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-025-00936-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00936-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:36:33Z","timestamp":1773480993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-025-00936-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,29]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["936"],"URL":"https:\/\/doi.org\/10.1007\/s41060-025-00936-3","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,29]]},"assertion":[{"value":"13 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 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":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"Not applicable","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"The authors declare no Conflict of interest.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"30"}}