{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:52:45Z","timestamp":1779889965113,"version":"3.53.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and\/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1186\/s40537-021-00467-1","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T13:09:59Z","timestamp":1622639399000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Using social media for sub-event detection during disasters"],"prefix":"10.1186","volume":"8","author":[{"given":"Loris","family":"Belcastro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7887-1314","authenticated-orcid":false,"given":"Fabrizio","family":"Marozzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domenico","family":"Talia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Trunfio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Branda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"issue":"1","key":"467_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-018-0547-5","volume":"9","author":"L Belcastro","year":"2018","unstructured":"Belcastro L, Marozzo F, Talia D, Trunfio P. Parsoda: High-level parallel programming for social data mining. Soc Netw Anal Min. 2018;9(1):1\u201319.","journal-title":"Soc Netw Anal Min"},{"key":"467_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.osnem.2017.10.002","volume":"3\u20134","author":"E Cesario","year":"2017","unstructured":"Cesario E, Marozzo F, Talia D, Trunfio P. Sma4td: a social media analysis methodology for trajectory discovery in large-scale events. Online Soc Netw Media. 2017;3\u20134:49\u201362.","journal-title":"Online Soc Netw Media"},{"key":"467_CR3","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.indmarman.2019.01.002","volume":"82","author":"C Ancillai","year":"2019","unstructured":"Ancillai C, Terho H, Cardinali S, Pascucci F. Advancing social media driven sales research: establishing conceptual foundations for b-to-b social selling. Indus Market Manage. 2019;82:293\u2013308.","journal-title":"Indus Market Manage"},{"key":"467_CR4","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.chb.2018.09.031","volume":"101","author":"C-w Shen","year":"2019","unstructured":"Shen C-w, Chen M, Wang C-c. Analyzing the trend of o2o commerce by bilingual text mining on social media. Comput Human Behav. 2019;101:474\u201383. https:\/\/doi.org\/10.1016\/j.chb.2018.09.031.","journal-title":"Comput Human Behav"},{"issue":"1","key":"467_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00429-7","volume":"8","author":"B Athira","year":"2021","unstructured":"Athira B, Jones J, Idicula SM, Kulanthaivel A, Zhang E. Annotating and detecting topics in social media forum and modelling the annotation to derive directions\u2014a case study. J Big Data. 2021;8(1):1\u201323.","journal-title":"J Big Data"},{"key":"467_CR6","doi-asserted-by":"crossref","unstructured":"Sarlan A, Nadam C, Basri S. Twitter sentiment analysis. In: Proceedings of the 6th IEEE international conference on information technology and multimedia; 2014. p. 212\u20136.","DOI":"10.1109\/ICIMU.2014.7066632"},{"issue":"4","key":"467_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3202662","volume":"36","author":"SE Middleton","year":"2018","unstructured":"Middleton SE, Kordopatis-Zilos G, Papadopoulos S, Kompatsiaris Y. Location extraction from social media: geoparsing, location disambiguation, and geotagging. ACM Trans Inform Syst (TOIS). 2018;36(4):1\u201327.","journal-title":"ACM Trans Inform Syst (TOIS)"},{"issue":"1","key":"467_CR8","doi-asserted-by":"publisher","first-page":"47177","DOI":"10.1109\/ACCESS.2020.2978950","volume":"8","author":"L Belcastro","year":"2020","unstructured":"Belcastro L, Cantini R, Marozzo F, Talia D, Trunfio P. Learning political polarization on social media using neural networks. IEEE Access. 2020;8(1):47177\u201387.","journal-title":"IEEE Access"},{"issue":"1","key":"467_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0216-1","volume":"6","author":"A Subroto","year":"2019","unstructured":"Subroto A, Apriyana A. Cyber risk prediction through social media big data analytics and statistical machine learning. J Big Data. 2019;6(1):1\u201319.","journal-title":"J Big Data"},{"issue":"1","key":"467_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00428-8","volume":"8","author":"M Roccetti","year":"2021","unstructured":"Roccetti M, Delnevo G, Casini L, Mirri S. An alternative approach to dimension reduction for pareto distributed data: a case study. J Big Data. 2021;8(1):1\u201323.","journal-title":"J Big Data"},{"issue":"1","key":"467_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. J Big Data. 2021;8(1):1\u201374.","journal-title":"J Big Data"},{"key":"467_CR12","first-page":"1","volume-title":"Lessons from Harvey: crisis informatics for urban resilience","author":"C Villegas","year":"2018","unstructured":"Villegas C, Martinez M, Krause M. Lessons from Harvey: crisis informatics for urban resilience. Rice University Kinder Institute for Urban Research; 2018. p. 1\u201320."},{"key":"467_CR13","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.future.2020.06.040","volume":"112","author":"M Raza","year":"2020","unstructured":"Raza M, Awais M, Ali K, Aslam N, Paranthaman VV, Imran M, Ali F. Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform. Fut Gen Comput Syst. 2020;112:1057\u201369.","journal-title":"Fut Gen Comput Syst"},{"issue":"1","key":"467_CR14","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1145\/3137597.3137602","volume":"19","author":"TH Nazer","year":"2017","unstructured":"Nazer TH, Xue G, Ji Y, Liu H. Intelligent disaster response via social media analysis a survey. ACM SIGKDD Explor Newsl. 2017;19(1):46\u201359.","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"5","key":"467_CR15","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/j.ijinfomgt.2015.07.001","volume":"35","author":"T Simon","year":"2015","unstructured":"Simon T, Goldberg A, Adini B. Socializing in emergencies-a review of the use of social media in emergency situations. Int J Inform Manage. 2015;35(5):609\u201319.","journal-title":"Int J Inform Manage"},{"issue":"22","key":"467_CR16","doi-asserted-by":"publisher","first-page":"31267","DOI":"10.1007\/s11042-019-07942-1","volume":"78","author":"N Said","year":"2019","unstructured":"Said N, Ahmad K, Riegler M, Pogorelov K, Hassan L, Ahmad N, Conci N. Natural disasters detection in social media and satellite imagery: a survey. Multimed Tools Appl. 2019;78(22):31267\u2013302.","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"467_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2771588","volume":"47","author":"M Imran","year":"2015","unstructured":"Imran M, Castillo C, Diaz F, Vieweg S. Processing social media messages in mass emergency: a survey. ACM Comput Surveys (CSUR). 2015;47(4):1\u201338.","journal-title":"ACM Comput Surveys (CSUR)"},{"issue":"1","key":"467_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1080\/13658816.2017.1367003","volume":"32","author":"Z Wang","year":"2018","unstructured":"Wang Z, Ye X. Social media analytics for natural disaster management. Int J Geogr Inform Sci. 2018;32(1):49\u201372.","journal-title":"Int J Geogr Inform Sci"},{"key":"467_CR19","doi-asserted-by":"publisher","first-page":"012039","DOI":"10.1088\/1757-899X\/288\/1\/012039","volume":"288","author":"C Slamet","year":"2018","unstructured":"Slamet C, Rahman A, Sutedi A, Darmalaksana W, Ramdhani MA, Maylawati DS. Social media-based identifier for natural disaster. IOP Conf Ser Mater Sci Eng. 2018;288:012039.","journal-title":"IOP Conf Ser Mater Sci Eng"},{"issue":"1","key":"467_CR20","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/TCSS.2017.2786545","volume":"5","author":"R Dong","year":"2018","unstructured":"Dong R, Li L, Zhang Q, Cai G. Information diffusion on social media during natural disasters. IEEE Trans Comput Soc Syst. 2018;5(1):265\u201376.","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"1","key":"467_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1111\/j.1467-9671.2012.01359.x","volume":"17","author":"A Crooks","year":"2013","unstructured":"Crooks A, Croitoru A, Stefanidis A, Radzikowski J. Earthquake: Twitter as a distributed sensor system. Trans GIS. 2013;17(1):124\u201347.","journal-title":"Trans GIS"},{"key":"467_CR22","doi-asserted-by":"crossref","unstructured":"Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, 2010; p. 851\u2013860","DOI":"10.1145\/1772690.1772777"},{"key":"467_CR23","doi-asserted-by":"crossref","unstructured":"Avvenuti M, Cresci S, Marchetti A, Meletti C, Tesconi M. Ears (earthquake alert and report system) a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014; p. 1749\u20131758","DOI":"10.1145\/2623330.2623358"},{"key":"467_CR24","doi-asserted-by":"crossref","unstructured":"From rapid information to global seismic risk reduction. Lastquake. Int J Disaster Risk Reduction. 2018;28:32\u201342.","DOI":"10.1016\/j.ijdrr.2018.02.024"},{"issue":"1","key":"467_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-017-0096-1","volume":"4","author":"M Sangameswar","year":"2017","unstructured":"Sangameswar M, Rao MN, Satyanarayana S. An algorithm for identification of natural disaster affected area. J Big Data. 2017;4(1):1\u201311.","journal-title":"J Big Data"},{"key":"467_CR26","doi-asserted-by":"crossref","unstructured":"Meladianos P, Xypolopoulos C, Nikolentzos G, Vazirgiannis M. An optimization approach for sub-event detection and summarization in twitter. In: European Conference on Information Retrieval, 2018; p. 481\u2013493. Springer.","DOI":"10.1007\/978-3-319-76941-7_36"},{"key":"467_CR27","doi-asserted-by":"crossref","unstructured":"Abhik D, Toshniwal D. Sub-event detection during natural hazards using features of social media data. In: Proceedings of the 22nd International Conference on World Wide Web. WWW\u201913 Companion, pp. 783\u2013788. Association for Computing Machinery,New York, NY, USA 2013.","DOI":"10.1145\/2487788.2488046"},{"key":"467_CR28","doi-asserted-by":"crossref","unstructured":"Nguyen DT, Al\u00a0Mannai KA, Joty S, Sajjad H, Imran M, Mitra P. Robust classification of crisis-related data on social networks using convolutional neural networks. In: Eleventh International AAAI Conference on Web and Social Media 2017.","DOI":"10.1609\/icwsm.v11i1.14950"},{"key":"467_CR29","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang Y. A neural model for joint event detection and summarization. In: IJCAI, 2017; p. 4158\u20134164.","DOI":"10.24963\/ijcai.2017\/581"},{"key":"467_CR30","doi-asserted-by":"crossref","unstructured":"Bekoulis G, Deleu J, Demeester T, Develder C. Sub-event detection from twitter streams as a sequence labeling problem. 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. 745\u2013750. Association for Computational Linguistics, Minneapolis, Minnesota 2019.","DOI":"10.18653\/v1\/N19-1081"},{"key":"467_CR31","doi-asserted-by":"crossref","unstructured":"Pohl D, Bouchachia A, Hellwagner H. Automatic sub-event detection in emergency management using social media. In: Proceedings of the 21st International Conference on World Wide Web, 2012; p. 683\u2013686 .","DOI":"10.1145\/2187980.2188180"},{"key":"467_CR32","doi-asserted-by":"crossref","unstructured":"Abhik D, Toshniwal D. Sub-event detection during natural hazards using features of social media data. In: Proceedings of the 22nd International Conference on World Wide Web, 2013; p. 783\u2013788 .","DOI":"10.1145\/2487788.2488046"},{"key":"467_CR33","unstructured":"Xing C, Wang Y, Liu, J, Huang Y, Ma W-Y. Hashtag-based sub-event discovery using mutually generative lda in twitter. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI\u201916, pp. 2666\u20132672. AAAI Press, Phoenix, Arizona, USA 2016."},{"issue":"4","key":"467_CR34","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1016\/j.ipm.2016.10.004","volume":"53","author":"P Srijith","year":"2017","unstructured":"Srijith P, Hepple M, Bontcheva K, Preotiuc-Pietro D. Sub-story detection in twitter with hierarchical dirichlet processes. Inform Process Manage. 2017;53(4):989\u20131003.","journal-title":"Inform Process Manage"},{"key":"467_CR35","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.future.2018.09.008","volume":"93","author":"D Nolasco","year":"2019","unstructured":"Nolasco D, Oliveira J. Subevents detection through topic modeling in social media posts. Fut Gen Comput Syst. 2019;93:290\u2013303.","journal-title":"Fut Gen Comput Syst"},{"key":"467_CR36","doi-asserted-by":"crossref","unstructured":"Rudra K, Goyal P, Ganguly N, Mitra P, Imran M. Identifying sub-events and summarizing disaster-related information from microblogs. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. SIGIR \u201918. New York: NY, USA; 2018. p. 265\u201374.","DOI":"10.1145\/3209978.3210030"},{"key":"467_CR37","doi-asserted-by":"crossref","unstructured":"Lourentzou I, Morales A, Zhai C. Text-based geolocation prediction of social media users with neural networks. In: 2017 IEEE International Conference on Big Data (Big Data), 2017; p.696\u2013705 IEEE.","DOI":"10.1109\/BigData.2017.8257985"},{"issue":"9","key":"467_CR38","first-page":"37","volume":"2014","author":"W Zhang","year":"2014","unstructured":"Zhang W, Gelernter J. Geocoding location expressions in twitter messages: a preference learning method. J Spatial Inform Sci. 2014;2014(9):37\u201370.","journal-title":"J Spatial Inform Sci"},{"issue":"8","key":"467_CR39","doi-asserted-by":"publisher","first-page":"e5638","DOI":"10.1002\/cpe.5638","volume":"33","author":"L Belcastro","year":"2021","unstructured":"Belcastro L, Kechadi MT, Marozzo F, Pastore L, Talia D, Trunfio P. Parallel extraction of regions-of-interest from social media data. Concurr Comput Pract Exp. 2021;33(8);e5638.","journal-title":"Concurr Comput Pract Exp"},{"key":"467_CR40","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD. 1996;96:226\u201331.","journal-title":"KDD"},{"issue":"3","key":"467_CR41","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/3154411","volume":"12","author":"L Belcastro","year":"2018","unstructured":"Belcastro L, Marozzo F, Talia D, Trunfio P. G-RoI: automatic region-of-interest detection driven by geotagged social media data. ACM Trans Knowl Discov Data. 2018;12(3):27\u201312722.","journal-title":"ACM Trans Knowl Discov Data"},{"key":"467_CR42","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. arXiv:1310.4546 (2013)"},{"key":"467_CR43","doi-asserted-by":"crossref","unstructured":"Cresci S, Tesconi M, Cimino A, Dell\u2019Orletta F. A linguistically-driven approach to cross-event damage assessment of natural disasters from social media messages. In: Proceedings of the 24th International Conference on World Wide Web, 2015; p. 1195\u20131200.","DOI":"10.1145\/2740908.2741722"},{"issue":"1","key":"467_CR44","first-page":"25","volume":"30","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Kanellopoulos D, Pintelas P, et al. Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng. 2006;30(1):25\u201336.","journal-title":"GESTS Int Trans Comput Sci Eng"},{"key":"467_CR45","doi-asserted-by":"crossref","unstructured":"Cooper C, Zito M. Realistic synthetic data for testing association rule mining algorithms for market basket databases. In: Kok JN, Koronacki J, Lopez\u00a0de Mantaras R, Matwin S, Mladeni\u010d D, Skowron A, editors. Knowledge discovery in databases: PKDD 2007, 2007; p. 398\u2013405 .","DOI":"10.1007\/978-3-540-74976-9_39"},{"issue":"3","key":"467_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3083897","volume":"42","author":"J Gan","year":"2017","unstructured":"Gan J, Tao Y. On the hardness and approximation of Euclidean dbscan. ACM Trans Database Syst (TODS). 2017;42(3):1\u201345.","journal-title":"ACM Trans Database Syst (TODS)"},{"key":"467_CR47","unstructured":"Ramos J., et al. Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, 2003; 242, p. 133\u2013142 . New Jersey, USA."},{"key":"467_CR48","doi-asserted-by":"crossref","unstructured":"Huang B, Carley KM. A large-scale empirical study of geotagging behavior on twitter. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019; p. 365\u2013373.","DOI":"10.1145\/3341161.3342870"},{"key":"467_CR49","unstructured":"Alam F, Ofli F, Imran M, Aupetit M. A twitter tale of three hurricanes: Harvey, irma, and maria. Rochester, USA: Proc. of ISCRAM; 2018."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00467-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-021-00467-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00467-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T12:38:52Z","timestamp":1672317532000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-021-00467-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,2]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["467"],"URL":"https:\/\/doi.org\/10.1186\/s40537-021-00467-1","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,2]]},"assertion":[{"value":"23 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2021","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":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"79"}}