{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:38:51Z","timestamp":1772152731394,"version":"3.50.1"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031585012","type":"print"},{"value":"9783031585029","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58502-9_2","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T14:02:19Z","timestamp":1714140139000},"page":"17-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Topic Modeling Applied to\u00a0Reddit Posts"],"prefix":"10.1007","author":[{"given":"Maria","family":"K\u0119dzierska","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miko\u0142aj","family":"Spytek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcelina","family":"Kurek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Sawicki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Ganzha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcin","family":"Paprzycki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"issue":"7347","key":"2_CR1","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1038\/473285a","volume":"473","author":"HA Piwowar","year":"2011","unstructured":"Piwowar, H.A., Vision, T.J., Whitlock, M.C.: Data archiving is a good investment. Nature 473(7347), 285 (2011)","journal-title":"Nature"},{"issue":"1","key":"2_CR2","first-page":"9","volume":"23","author":"J Sawicki","year":"2022","unstructured":"Sawicki, J., Ganzha, M., Paprzycki, M., Badica, A.: Exploring usability of reddit in data science and knowledge processing. Scalable Comput.: Pract. Exp. 23(1), 9\u201322 (2022)","journal-title":"Scalable Comput.: Pract. Exp."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Proferes, N., Jones, N., Gilbert, S., Fiesler, C., Zimmer, M.: Studying reddit: a systematic overview of disciplines, approaches, methods, and ethics. Soc. Media + Soc. 7(2) (2021)","DOI":"10.1177\/20563051211019004"},{"key":"2_CR4","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":"2_CR5","unstructured":"Grootendorst, M.: BERTopic: neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022)"},{"issue":"6755","key":"2_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788\u2013791 (1999)","journal-title":"Nature"},{"issue":"1","key":"2_CR7","first-page":"5","volume":"22","author":"MR Jamnik","year":"2017","unstructured":"Jamnik, M.R., Lane, D.J.: The use of reddit as an inexpensive source for highquality data. Pract. Assess. Res. Eval. 22(1), 5 (2017)","journal-title":"Pract. Assess. Res. Eval."},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"De Candia, S., De Francisci Morales, G., Monti, C., Bonchi, F.: Social norms on reddit: a demographic analysis. In: 14th ACM Web Science Conference 2022, pp. 139\u2013147 (2022)","DOI":"10.1145\/3501247.3531549"},{"issue":"1","key":"2_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s40806-018-0163-7","volume":"5","author":"M Apostolou","year":"2019","unstructured":"Apostolou, M.: Why men stay single? Evidence from reddit. Evol. Psychol. Sci. 5(1), 87\u201397 (2019)","journal-title":"Evol. Psychol. Sci."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Zomick, J., Levitan, S.I., Serper, M.: Linguistic analysis of schizophrenia in reddit posts. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 74\u201383 (2019)","DOI":"10.18653\/v1\/W19-3009"},{"issue":"5","key":"2_CR11","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1177\/0894439319893305","volume":"39","author":"A Amaya","year":"2021","unstructured":"Amaya, A., Bach, R., Keusch, F., Kreuter, F.: New data sources in social science research: things to know before working with reddit data. Soc. Sci. Comput. Rev. 39(5), 943\u2013960 (2021)","journal-title":"Soc. Sci. Comput. Rev."},{"issue":"10s","key":"2_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3507900","volume":"54","author":"R Churchill","year":"2022","unstructured":"Churchill, R., Singh, L.: The evolution of topic modeling. ACM Comput. Surv. 54(10s), 1\u201335 (2022)","journal-title":"ACM Comput. Surv."},{"key":"2_CR13","unstructured":"Kherwa, P., Bansal, P.: Topic modeling: a comprehensive review. EAI Endors. Trans. Scalable Inf. Syst. 7(24) (2019)"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"101582","DOI":"10.1016\/j.is.2020.101582","volume":"94","author":"I Vayansky","year":"2020","unstructured":"Vayansky, I., Kumar, S.A.: A review of topic modeling methods. Inf. Syst. 94, 101582 (2020)","journal-title":"Inf. Syst."},{"issue":"2\u20133","key":"2_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1080\/01638539809545028","volume":"25","author":"TK Landauer","year":"1998","unstructured":"Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2\u20133), 259\u2013284 (1998)","journal-title":"Discourse Process."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Hofmann, T.: Probabilistic latent semantic analysis. In: Uncertainty in Artificial Intelligence (UAI 99), Stockholm, Sweden (1999)","DOI":"10.1145\/312624.312649"},{"key":"2_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"2_CR18","first-page":"2","volume":"3","author":"R Rehurek","year":"2011","unstructured":"Rehurek, R., Sojka, P.: Gensim - python framework for vector space modelling. NLP Cent. Fac. Inform. Masaryk Univ. Brno Czech Republic 3(2), 2 (2011)","journal-title":"NLP Cent. Fac. Inform. Masaryk Univ. Brno Czech Republic"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"113401","DOI":"10.1016\/j.eswa.2020.113401","volume":"152","author":"S Kim","year":"2020","unstructured":"Kim, S., Park, H., Lee, J.: Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: a study on blockchain technology trend analysis. Expert Syst. Appl. 152, 113401 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40064-016-3252-8","volume":"5","author":"L Liu","year":"2016","unstructured":"Liu, L., Tang, L., Dong, W., Yao, S., Zhou, W.: An overview of topic modeling and its current applications in bioinformatics. Springerplus 5(1), 1\u201322 (2016)","journal-title":"Springerplus"},{"key":"2_CR21","unstructured":"Ramage, D., Rosen, E., Chuang, J., Manning, C.D., McFarland, D.A.: Topic modeling for the social sciences. In: NIPS 2009 Workshop on Applications for Topic Models: Text and Beyond, vol. 5, pp. 1\u20134 (2009)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Terragni, S., Fersini, E., Galuzzi, B.G., Tropeano, P., Candelieri, A.: OCTIS: comparing and optimizing topic models is simple! In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pp. 263\u2013270 (2021)","DOI":"10.18653\/v1\/2021.eacl-demos.31"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Lau, J.H., Newman, D., Baldwin, T.: Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 530\u2013539 (2014)","DOI":"10.3115\/v1\/E14-1056"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"R\u00f6der, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399\u2013408 (2015)","DOI":"10.1145\/2684822.2685324"},{"key":"2_CR25","doi-asserted-by":"publisher","first-page":"127531","DOI":"10.1109\/ACCESS.2021.3112620","volume":"9","author":"S Latif","year":"2021","unstructured":"Latif, S., Shafait, F., Latif, R., et al.: Analyzing LDA and NMF topic models for Urdu tweets via automatic labeling. IEEE Access 9, 127531\u2013127547 (2021)","journal-title":"IEEE Access"},{"issue":"4","key":"2_CR26","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1080\/13548506.2020.1738019","volume":"26","author":"S Liu","year":"2021","unstructured":"Liu, S., Zhang, R.-Y., Kishimoto, T.: Analysis and prospect of clinical psychology based on topic models: hot research topics and scientific trends in the latest decades. Psychol. Health Med. 26(4), 395\u2013407 (2021)","journal-title":"Psychol. Health Med."},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Bianchi, F., Terragni, S., Hovy, D.: Pre-training is a hot topic: contextualized document embeddings improve topic coherence. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 759\u2013766. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2021.acl-short.96"},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1162\/tacl_a_00325","volume":"8","author":"AB Dieng","year":"2020","unstructured":"Dieng, A.B., Ruiz, F.J., Blei, D.M.: Topic modeling in embedding spaces. Trans. Assoc. Comput. Linguist. 8, 439\u2013453 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Keane, N., Yee, C., Zhou, L.: Using topic modeling and similarity thresholds to detect events. In: Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation, pp. 34\u201342 (2015)","DOI":"10.3115\/v1\/W15-0805"},{"key":"2_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-642-04180-8_22","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"L AlSumait","year":"2009","unstructured":"AlSumait, L., Barbar\u00e1, D., Gentle, J., Domeniconi, C.: Topic significance ranking of LDA generative models. In: Buntine, W., Grobelnik, M., Mladeni\u0107, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5781, pp. 67\u201382. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04180-8_22"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Terragni, S., Nozza, D., Fersini, E., Enza, M.: Which matters most? Comparing the impact of concept and document relationships in topic models. In: Proceedings of the First Workshop on Insights from Negative Results in NLP, pp. 32\u201340 (2020)","DOI":"10.18653\/v1\/2020.insights-1.5"},{"issue":"11","key":"2_CR32","doi-asserted-by":"publisher","first-page":"20550","DOI":"10.2196\/20550","volume":"22","author":"J Xue","year":"2020","unstructured":"Xue, J., et al.: Twitter discussions and emotions about the COVID-19 pandemic: machine Learning approach. J. Med. Internet Res. 22(11), 20550 (2020)","journal-title":"J. Med. Internet Res."},{"key":"2_CR33","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.tourman.2016.09.009","volume":"59","author":"Y Guo","year":"2017","unstructured":"Guo, Y., Barnes, S.J., Jia, Q.: Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent Dirichlet allocation. Tour. Manage. 59, 467\u2013483 (2017)","journal-title":"Tour. Manage."},{"key":"2_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-642-29047-3_28","volume-title":"Social Computing, Behavioral - Cultural Modeling and Prediction","author":"X Wang","year":"2012","unstructured":"Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from twitter posts. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 231\u2013238. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-29047-3_28"},{"issue":"11","key":"2_CR35","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar, H., et al.: Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed. Tools Appl. 78(11), 15169\u201315211 (2019)","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"2_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-022-00898-5","volume":"12","author":"S Athukorala","year":"2022","unstructured":"Athukorala, S., Mohotti, W.: An effective short-text topic modelling with neighbourhood assistance-driven NMF in twitter. Soc. Netw. Anal. Min. 12(1), 1\u201315 (2022)","journal-title":"Soc. Netw. Anal. Min."},{"key":"2_CR37","doi-asserted-by":"publisher","first-page":"104034","DOI":"10.1016\/j.jbi.2022.104034","volume":"128","author":"C Meaney","year":"2022","unstructured":"Meaney, C., et al.: Non-negative matrix factorization temporal topic models and clinical text data identify COVID-19 pandemic effects on primary healthcare and community health in Toronto. Canada. J. Biomed. Inform. 128, 104034 (2022)","journal-title":"Canada. J. Biomed. Inform."},{"key":"2_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2018.08.011","volume":"163","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Zhang, H., Liu, R., Ye, Z., Lin, J.: Experimental explorations on short text topic mining between LDA and NMF based schemes. Knowl.-Based Syst. 163, 1\u201313 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Suri, P., Roy, N.R.: Comparison between LDA & NMF for event-detection from large text stream data. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/CIACT.2017.7977281"},{"key":"2_CR40","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis (2019)"},{"issue":"29","key":"2_CR41","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: uniform manifold approximation and projection. J. Open Sour. Softw. 3(29), 861 (2018)","journal-title":"J. Open Sour. Softw."},{"issue":"11","key":"2_CR42","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Sour. Softw. 2(11), 205 (2017)","journal-title":"J. Open Sour. Softw."},{"key":"2_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.puhe.2022.09.008","volume":"213","author":"Q Ng","year":"2022","unstructured":"Ng, Q., Yau, C., Lim, Y., Wong, L., Liew, T.: Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts. Public Health 213, 1\u20134 (2022)","journal-title":"Public Health"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Clapham, B., Bender, M., Lausen, J., Gomber, P.: Policy making in the financial industry: a framework for regulatory impact analysis using textual analysis. J. Bus. Econ. 1\u201352 (2022)","DOI":"10.1007\/s11573-022-01119-3"},{"key":"2_CR45","unstructured":"Belford, M., Greene, D.: Comparison of embedding techniques for topic modeling coherence measures. In: Proceedings of the Poster Session of the 2nd Conference (2019)"},{"key":"2_CR46","unstructured":"Mikolov, T., Yih, W.-T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746\u2013751 (2013)"},{"key":"2_CR47","unstructured":"Rosner, F., Hinneburg, A., R\u00f6der, M., Nettling, M., Both, A.: Evaluating topic coherence measures. arXiv preprint arXiv:1403.6397 (2014)"},{"issue":"13","key":"2_CR48","doi-asserted-by":"publisher","first-page":"5645","DOI":"10.1016\/j.eswa.2015.02.055","volume":"42","author":"D O\u2019callaghan","year":"2015","unstructured":"O\u2019callaghan, D., Greene, D., Carthy, J., Cunningham, P.: An analysis of the coherence of descriptors in topic modeling. Expert Syst. Appl. 42(13), 5645\u20135657 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"2_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1852102.1852106","volume":"28","author":"W Webber","year":"2010","unstructured":"Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (TOIS) 28(4), 1\u201338 (2010)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"issue":"4","key":"2_CR50","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1093\/pan\/mpn018","volume":"16","author":"BL Monroe","year":"2017","unstructured":"Monroe, B.L., Colaresi, M.P., Quinn, K.M.: Fightin\u2019 words: lexical feature selection and evaluation for identifying the content of political conflict. Polit. Anal. 16(4), 372\u2013403 (2017)","journal-title":"Polit. Anal."},{"issue":"1","key":"2_CR51","first-page":"7","volume":"5","author":"S Kannan","year":"2014","unstructured":"Kannan, S., et al.: Preprocessing techniques for text mining. Int. J. Comput. Sci. Commun. Netw. 5(1), 7\u201316 (2014)","journal-title":"Int. J. Comput. Sci. Commun. Netw."},{"key":"2_CR52","doi-asserted-by":"crossref","unstructured":"Sun, X., Liu, X., Hu, J., Zhu, J.: Empirical studies on the NLP techniques for source code data preprocessing. In: Proceedings of the 2014 3rd International Workshop on Evidential Assessment of Software Technologies, pp. 32\u201339 (2014)","DOI":"10.1145\/2627508.2627514"},{"key":"2_CR53","unstructured":"Python, W.: Python. Python Releases for Windows 24 (2021)"},{"key":"2_CR54","unstructured":"Hardeniya, N., Perkins, J., Chopra, D., Joshi, N., Mathur, I.: Natural Language Processing: Python and NLTK. Packt Publishing Ltd. (2016)"},{"key":"2_CR55","unstructured":"Vasiliev, Y.: Natural Language Processing with Python and spaCy: A Practical Introduction. No Starch Press (2020)"},{"key":"2_CR56","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"5","key":"2_CR57","doi-asserted-by":"publisher","first-page":"81","DOI":"10.26483\/ijarcs.v9i5.6301","volume":"9","author":"J Kaur","year":"2018","unstructured":"Kaur, J., Buttar, P.K.: Stopwords removal and its algorithms based on different methods. Int. J. Adv. Res. Comput. Sci. 9(5), 81\u201388 (2018)","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"2_CR58","unstructured":"Nicoletti, P.: IEEE 802.11 frame format. XP055083596 (2005)"},{"key":"2_CR59","doi-asserted-by":"crossref","unstructured":"Sawicki, J., Ganzha, M., Paprzycki, M., B\u0103dic\u0103, A.: Exploring usability of reddit in data science and knowledge processing. arXiv preprint arXiv:2110.02158 (2021)","DOI":"10.12694\/scpe.v23i1.1957"}],"container-title":["Lecture Notes in Computer Science","Big Data Analytics in Astronomy, Science, and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58502-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T14:02:48Z","timestamp":1714140168000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58502-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031585012","9783031585029"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58502-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aizu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigda2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/web-ext.u-aizu.ac.jp\/labs\/is-ds\/BDA2023-Aizu.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}