{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T10:27:59Z","timestamp":1758968879400,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T00:00:00Z","timestamp":1585353600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T00:00:00Z","timestamp":1585353600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"MINECO\/FEDER","award":["SomEMBED TIN2015-71147-C2-1-P"],"award-info":[{"award-number":["SomEMBED TIN2015-71147-C2-1-P"]}]},{"DOI":"10.13039\/501100000269","name":"UK Economic and Social Research Council","doi-asserted-by":"crossref","award":["ES\/M010236\/1"],"award-info":[{"award-number":["ES\/M010236\/1"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Lang Resources & Evaluation"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s10579-020-09486-5","type":"journal-article","created":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T05:02:55Z","timestamp":1585371775000},"page":"1019-1058","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Fake opinion detection: how similar are crowdsourced datasets to real data?"],"prefix":"10.1007","volume":"54","author":[{"given":"Tommaso","family":"Fornaciari","sequence":"first","affiliation":[]},{"given":"Leticia","family":"Cagnina","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[]},{"given":"Massimo","family":"Poesio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"issue":"6","key":"9486_CR1","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1145\/3209581","volume":"61","author":"R Baeza-Yates","year":"2018","unstructured":"Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61(6), 54\u201361.","journal-title":"Communications of the ACM"},{"key":"9486_CR2","unstructured":"Banerjee, S., & Chua, A. Y. (2014). Applauses in hotel reviews: Genuine or deceptive? In: Science and Information Conference (SAI), 2014 (pp. 938\u2013942). New York: IEEE."},{"key":"9486_CR3","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2017-0501","author":"R Bhargava","year":"2018","unstructured":"Bhargava, R., Baoni, A., & Sharma, Y. (2018). Composite sequential modeling for identifying fake reviews. Journal of Intelligent Systems,. https:\/\/doi.org\/10.1515\/jisys-2017-0501.","journal-title":"Journal of Intelligent Systems"},{"key":"9486_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/b20576","volume-title":"Mathematical statistics: Basic ideas and selected topics","author":"PJ Bickel","year":"2015","unstructured":"Bickel, P. J., & Doksum, K. A. (2015). Mathematical statistics: Basic ideas and selected topics (2nd ed., Vol. 1). Boca Raton: Chapman and Hall\/CRC Press.","edition":"2"},{"issue":"Jan","key":"9486_CR5","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993\u20131022.","journal-title":"Journal of Machine Learning Research"},{"key":"9486_CR6","unstructured":"Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory (pp. 92\u2013100). New York: ACM."},{"issue":"Suppl. 2","key":"9486_CR7","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1142\/S0218488517400165","volume":"25","author":"LC Cagnina","year":"2017","unstructured":"Cagnina, L. C., & Rosso, P. (2017). Detecting deceptive opinions: Intra and cross-domain classification using an efficient representation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2), 151\u2013174. https:\/\/doi.org\/10.1142\/S0218488517400165.","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"key":"9486_CR8","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2018.04.074","volume":"309","author":"EF Cardoso","year":"2018","unstructured":"Cardoso, E. F., Silva, R. M., & Almeida, T. A. (2018). Towards automatic filtering of fake reviews. Neurocomputing, 309, 106\u2013116. https:\/\/doi.org\/10.1016\/j.neucom.2018.04.074.","journal-title":"Neurocomputing"},{"key":"9486_CR9","unstructured":"Carpenter, B. (2008). Multilevel bayesian models of categorical data annotation. Retrieved from http:\/\/lingpipe.files.wordpress.com\/2008\/11\/carp-bayesian-multilevel-annotation.pdf."},{"key":"9486_CR10","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273\u2013297.","journal-title":"Machine Learning"},{"key":"9486_CR11","unstructured":"Costa, P. T., & MacCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Professional manual. Psychological Assessment Resources."},{"issue":"1","key":"9486_CR12","doi-asserted-by":"publisher","first-page":"20","DOI":"10.2307\/2346806","volume":"28","author":"AP Dawid","year":"1979","unstructured":"Dawid, A. P., & Skene, A. M. (1979). Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied Statistics, 28(1), 20\u201328.","journal-title":"Applied Statistics"},{"issue":"1","key":"9486_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1), 1\u201338.","journal-title":"Journal of the Royal Statistical Society Series B (Methodological)"},{"key":"9486_CR14","unstructured":"Elkan, C., & Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 213\u2013220). New York: ACM."},{"key":"9486_CR15","unstructured":"Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (Vol. 13, pp. 175\u2013184)."},{"key":"9486_CR16","unstructured":"Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Vol. 2: Short Papers, pp. 171\u2013175). Jeju Island: Association for Computational Linguistics."},{"key":"9486_CR17","first-page":"1289","volume":"3","author":"G Forman","year":"2003","unstructured":"Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289\u20131305.","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"9486_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s10506-013-9140-4","volume":"21","author":"T Fornaciari","year":"2013","unstructured":"Fornaciari, T., & Poesio, M. (2013). Automatic deception detection in Italian court cases. Artificial intelligence and law, 21(3), 303\u2013340. https:\/\/doi.org\/10.1007\/s10506-013-9140-4.","journal-title":"Artificial intelligence and law"},{"key":"9486_CR19","unstructured":"Fornaciari, T., & Poesio, M. (2014). Identifying fake amazon reviews as learning from crowds. In: Proceedings of the 14th conference of the European chapter of the Association for Computational Linguistics (pp. 279\u2013287). Gothenburg: Association for Computational Linguistics. Retrieved from http:\/\/www.aclweb.org\/anthology\/E14-1030."},{"key":"9486_CR20","series-title":"Analytical methods for social research","volume-title":"Data analysis using regression and multilevel\/hierarchical models","author":"A Gelman","year":"2007","unstructured":"Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel\/hierarchical models., Analytical methods for social research Cambridge: Cambridge University Press."},{"key":"9486_CR21","unstructured":"Graves, A., Jaitly, N., & Mohamed, A. R. (2013). Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding (ASRU) (pp. 273\u2013278). New York: IEEE."},{"issue":"3","key":"9486_CR22","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3233\/IDA-170882","volume":"21","author":"\u00c1 Hern\u00e1ndez-Casta\u00f1eda","year":"2017","unstructured":"Hern\u00e1ndez-Casta\u00f1eda, \u00c1., & Calvo, H. (2017). Deceptive text detection using continuous semantic space models. Intelligent Data Analysis, 21(3), 679\u2013695.","journal-title":"Intelligent Data Analysis"},{"key":"9486_CR23","unstructured":"Hern\u00e1ndez\u00a0Fusilier, D., Guzm\u00e1n, R., M\u00f3ntes\u00a0y Gomez, M., & Rosso, P. (2013). Using pu-learning to detect deceptive opinion spam. In: Proc. of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 38\u201345)."},{"issue":"4","key":"9486_CR24","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.ipm.2014.11.001","volume":"51","author":"D Hern\u00e1ndez Fusilier","year":"2015","unstructured":"Hern\u00e1ndez Fusilier, D., Montes-y G\u00f3mez, M., Rosso, P., & Cabrera, R. G. (2015). Detecting positive and negative deceptive opinions using pu-learning. Information Processing & Management, 51(4), 433\u2013443.","journal-title":"Information Processing & Management"},{"key":"9486_CR25","unstructured":"Hovy, D. (2016). The enemy in your own camp: How well can we detect statistically-generated fake reviews\u2013an adversarial study. In: The 54th annual meeting of the association for computational linguistics (p 351)."},{"key":"9486_CR26","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-642-76626-8_35","volume-title":"Speech recognition and understanding","author":"F Jelinek","year":"1992","unstructured":"Jelinek, F., Lafferty, J. D., & Mercer, R. L. (1992). Basic methods of probabilistic context free grammars. Speech recognition and understanding (pp. 345\u2013360). New York: Springer."},{"key":"9486_CR27","unstructured":"Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining (pp. 219\u2013230). New York: ACM."},{"issue":"9","key":"9486_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v015.i09","volume":"15","author":"A Karatzoglou","year":"2006","unstructured":"Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software, 15(9), 1\u201328.","journal-title":"Journal of Statistical Software"},{"key":"9486_CR29","unstructured":"Kim, S., Lee, S., Park, D., & Kang, J. (2017). Constructing and evaluating a novel crowdsourcing-based paraphrased opinion spam dataset. In: Proceedings of the 26th international conference on world wide web (pp. 827\u2013836). Geneva: International World Wide Web Conferences Steering Committee."},{"issue":"3","key":"9486_CR30","first-page":"2488","volume":"22","author":"F Li","year":"2011","unstructured":"Li, F., Huang, M., Yang, Y., & Zhu, X. (2011). Learning to identify review spam. IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 22(3), 2488\u20132493.","journal-title":"IJCAI Proceedings-International Joint Conference on Artificial Intelligence"},{"key":"9486_CR31","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Z., Liu, B., Wei, X., & Shao, J. (2014a). Spotting fake reviews via collective positive-unlabeled learning. In: 2014 IEEE international conference on data mining (ICDM) (pp. 899\u2013904). New York: IEEE.","DOI":"10.1109\/ICDM.2014.47"},{"key":"9486_CR33","unstructured":"Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017). Bimodal distribution and co-bursting in review spam detection. In: Proceedings of the 26th international conference on world wide web (pp. 1063\u20131072). Geneva: International World Wide Web Conferences Steering Committee."},{"issue":"3","key":"9486_CR32","doi-asserted-by":"publisher","first-page":"467","DOI":"10.13053\/cys-18-3-2035","volume":"18","author":"H Li","year":"2014","unstructured":"Li, H., Liu, B., Mukherjee, A., & Shao, J. (2014b). Spotting fake reviews using positive-unlabeled learning. Computaci\u00f3n y Sistemas, 18(3), 467\u2013475.","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"9486_CR34","doi-asserted-by":"crossref","unstructured":"Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014c). Towards a general rule for identifying deceptive opinion spam. In: ACL (Vol. 1, pp. 1566\u20131576).","DOI":"10.3115\/v1\/P14-1147"},{"key":"9486_CR35","unstructured":"Lin, C. H., Hsu, P. Y., Cheng, M. S., Lei, H. T., & Hsu, M. C. (2017). Identifying deceptive review comments with rumor and lie theories. In: International conference in swarm intelligence (pp. 412\u2013420). New York: Springer."},{"key":"9486_CR37","unstructured":"Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using positive and unlabeled examples. In: Third IEEE international conference on data mining (pp. 179\u2013186). New York: IEEE."},{"key":"9486_CR36","first-page":"387","volume":"2","author":"B Liu","year":"2002","unstructured":"Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text documents. ICML, 2, 387\u2013394.","journal-title":"ICML"},{"key":"9486_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09706-9","author":"D Martens","year":"2019","unstructured":"Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering,. https:\/\/doi.org\/10.1007\/s10664-019-09706-9.","journal-title":"Empirical Software Engineering"},{"key":"9486_CR39","unstructured":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781."},{"key":"9486_CR40","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013a). Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 632\u2013640) New York: ACM.","DOI":"10.1145\/2487575.2487580"},{"key":"9486_CR41","unstructured":"Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. S. (2013b). What yelp fake review filter might be doing? In: Proceedings of the seventh international AAAI conference on weblogs and social media."},{"key":"9486_CR42","unstructured":"Negri, M., Bentivogli, L., Mehdad, Y., Giampiccolo, D., & Marchetti, A. (2011). Divide and conquer: Crowdsourcing the creation of cross-lingual textual entailment corpora. In: Proceedings of the conference on empirical methods in natural language processing (pp. 670\u2013679). Stroudsburg: Association for Computational Linguistics."},{"key":"9486_CR44","unstructured":"Ott, M., Cardie, C., & Hancock, J. T. (2013). Negative deceptive opinion spam. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 497\u2013501)."},{"key":"9486_CR43","unstructured":"Ott, M., Choi, Y., Cardie, C., & Hancock, J. (2011). Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual meeting of the association for computational linguistics: human language technologies (pp. 309\u2013319). Portland, Oregon: Association for Computational Linguistics."},{"key":"9486_CR45","volume-title":"Linguistic inquiry and word count (LIWC): LIWC2001","author":"JW Pennebaker","year":"2001","unstructured":"Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count (LIWC): LIWC2001. Mahwah: Lawrence Erlbaum Associates."},{"key":"9486_CR46","unstructured":"Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543)."},{"key":"9486_CR47","first-page":"1297","volume":"11","author":"VC Raykar","year":"2010","unstructured":"Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., et al. (2010). Learning from crowds. Journal of Machine Learning Research, 11, 1297\u20131322.","journal-title":"Journal of Machine Learning Research"},{"key":"9486_CR48","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ins.2017.01.015","volume":"385","author":"Y Ren","year":"2017","unstructured":"Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An empirical study. Information Sciences, 385, 213\u2013224.","journal-title":"Information Sciences"},{"issue":"1","key":"9486_CR49","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1109\/ACCESS.2017.2655032","volume":"5","author":"JK Rout","year":"2017","unstructured":"Rout, J. K., Dalmia, A., Choo, K. K. R., Bakshi, S., & Jena, S. K. (2017). Revisiting semi-supervised learning for online deceptive review detection. IEEE Access, 5(1), 1319\u20131327.","journal-title":"IEEE Access"},{"issue":"2","key":"9486_CR50","first-page":"84","volume":"12","author":"M Saini","year":"2017","unstructured":"Saini, M., & Sharan, A. (2017). Ensemble learning to find deceptive reviews using personality traits and reviews specific features. Journal of Digital Information Management, 12(2), 84\u201394.","journal-title":"Journal of Digital Information Management"},{"key":"9486_CR51","unstructured":"Salloum, W., Edwards, E., Ghaffarzadegan, S., Suendermann-Oeft, D., & Miller, M. (2017). Crowdsourced continuous improvement of medical speech recognition. In: The AAAI-17 workshop on crowdsourcing, deep learning, and artificial intelligence agents."},{"key":"9486_CR52","unstructured":"Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In: Proceedings of international conference on new methods in language processing. Retrieved from http:\/\/www.ims.uni-stuttgart.de\/ftp\/pub\/corpora\/tree-tagger1.pdf."},{"issue":"7","key":"9486_CR53","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/TIFS.2017.2675361","volume":"12","author":"S Shehnepoor","year":"2017","unstructured":"Shehnepoor, S., Salehi, M., Farahbakhsh, R., & Crespi, N. (2017). Netspam: A network-based spam detection framework for reviews in online social media. IEEE Transactions on Information Forensics and Security, 12(7), 1585\u20131595.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"9486_CR54","unstructured":"Skeppstedt, M., Peldszus, A., & Stede, M. (2018). More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing. In: Proceedings of the 5th workshop on argument mining (pp. 155\u2013163)."},{"key":"9486_CR55","unstructured":"Strapparava, C., & Mihalcea, R. (2009). The lie detector: Explorations in the automatic recognition of deceptive language. In: Proceedings of the 47th annual meeting of the association for computational linguistics and the 4th international joint conference on natural language processing."},{"key":"9486_CR56","unstructured":"Streitfeld, D. (August $$25{{\\rm th}}$$, 2012). The best book reviews money can buy. The New York Times."},{"key":"9486_CR57","first-page":"2035","volume-title":"Advances in neural information processing systems","author":"J Whitehill","year":"2009","unstructured":"Whitehill, J., Wu, T., Bergsma, F., Movellan, J. R., & Ruvolo, P. L. (2009). Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. Advances in neural information processing systems (pp. 2035\u20132043). Cambridge: MIT Press."},{"key":"9486_CR58","unstructured":"Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012). Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 823\u2013831). New York: ACM."},{"key":"9486_CR59","unstructured":"Yang, Y., & Liu, X. (1999). A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR \u201999 (pp. 42\u201349). New York: ACM."},{"issue":"1","key":"9486_CR60","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/info7010012","volume":"7","author":"W Zhang","year":"2016","unstructured":"Zhang, W., Bu, C., Yoshida, T., & Zhang, S. (2016). Cospa: A co-training approach for spam review identification with support vector machine. Information, 7(1), 12.","journal-title":"Information"},{"issue":"4","key":"9486_CR61","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.ipm.2018.03.007","volume":"54","author":"W Zhang","year":"2018","unstructured":"Zhang, W., Du, Y., Yoshida, T., & Wang, Q. (2018). DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Information Processing & Management, 54(4), 576\u2013592.","journal-title":"Information Processing & Management"},{"issue":"8","key":"9486_CR62","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1109\/TKDE.2007.190624","volume":"20","author":"L Zhou","year":"2008","unstructured":"Zhou, L., Shi, Y., & Zhang, D. (2008). A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering, 20(8), 1077\u20131081.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Language Resources and Evaluation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-020-09486-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10579-020-09486-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-020-09486-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T00:27:45Z","timestamp":1616891265000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10579-020-09486-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,28]]},"references-count":62,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["9486"],"URL":"https:\/\/doi.org\/10.1007\/s10579-020-09486-5","relation":{},"ISSN":["1574-020X","1574-0218"],"issn-type":[{"type":"print","value":"1574-020X"},{"type":"electronic","value":"1574-0218"}],"subject":[],"published":{"date-parts":[[2020,3,28]]},"assertion":[{"value":"28 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}