{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:11:22Z","timestamp":1780636282346,"version":"3.54.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"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"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot accounts have malicious intent, identifying bot accounts in general is an important and difficult task. In the literature there are three distinct types of feature sets one could use for building machine learning models for classifying bot accounts. These feature-sets are: user profile metadata, natural language features (\n                    <jats:bold>NLP<\/jats:bold>\n                    ) extracted from user tweets and finally features extracted from the the underlying social network. Profile metadata and\n                    <jats:bold>NLP<\/jats:bold>\n                    features are typically explored in detail in the bot-detection literature. At the same time less attention has been given to the predictive power of features that can be extracted from the underlying network structure. To fill this gap we explore and compare two classes of embedding algorithms that can be used to take advantage of information that network structure provides. The first class are classical embedding techniques, which focus on learning proximity information. The second class are structural embedding algorithms, which capture the local structure of node neighbourhood. We show that features created using structural embeddings have higher predictive power when it comes to bot detection. This supports the hypothesis that the local social network formed around bot accounts on Twitter contains valuable information that can be used to identify bot accounts.\n                  <\/jats:p>","DOI":"10.1186\/s40537-023-00796-3","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T14:09:36Z","timestamp":1689775776000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Detecting bots in social-networks using node and structural embeddings"],"prefix":"10.1186","volume":"10","author":[{"given":"Ashkan","family":"Dehghan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kinga","family":"Siuta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agata","family":"Skorupka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshat","family":"Dubey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Betlen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogumi\u0142","family":"Kami\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pawe\u0142","family":"Pra\u0142at","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"796_CR1","unstructured":"Ahmed Nesreen\u00a0K, Rossi Ryan\u00a0A, Lee John\u00a0Boaz, Willke Theodore\u00a0L, Zhou Rong, Kong Xiangnan, Eldardiry Hoda. role2vec: Role-based network embeddings. In Proc. DLG KDD, 2019;1\u20137."},{"key":"796_CR2","unstructured":"Aiello Luca\u00a0Maria, Deplano Martina, Schifanella Rossano, Ruffo Giancarlo. People are strange when you\u2019re a stranger: Impact and influence of bots on social networks. In Sixth International AAAI Conference on Weblogs and Social Media, 2012."},{"key":"796_CR3","doi-asserted-by":"crossref","unstructured":"Ali\u00a0Alhosseini Seyed, Bin\u00a0Tareaf Raad, Najafi Pejman, Meinel Christoph. Detect me if you can: Spam bot detection using inductive representation learning. In Companion Proceedings of The 2019 World Wide Web Conference, 2019;pages 148\u2013153.","DOI":"10.1145\/3308560.3316504"},{"key":"796_CR4","doi-asserted-by":"crossref","unstructured":"Alkulaib Lulwah, Zhang Lei, Sun Yanshen, Lu Chang-Tien. Twitter bot identification: An anomaly detection approach. In 2022 IEEE International Conference on Big Data (Big Data), pages 3577\u20133585. IEEE, 2022.","DOI":"10.1109\/BigData55660.2022.10020919"},{"issue":"1","key":"796_CR5","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1073\/pnas.1906420116","volume":"117","author":"Christopher\u00a0A Bail","year":"2020","unstructured":"Bail Christopher\u00a0A, Guay Brian, Maloney Emily, Combs Aidan, Hillygus D\u00a0Sunshine, Merhout Friedolin, Freelon Deen, Volfovsky Alexander. Assessing the Russian internet research agency\u2019s impact on the political attitudes and behaviors of American twitter users in late 2017. Proc Natl Acad Sci. 2020;117(1):243\u201350.","journal-title":"Proc Natl Acad Sci"},{"key":"796_CR6","doi-asserted-by":"crossref","unstructured":"Bojanowski Piotr, Grave Edouard, Joulin Armand, Mikolov Tomas. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016.","DOI":"10.1162\/tacl_a_00051"},{"key":"796_CR7","unstructured":"Brown Tom\u00a0B, Mann Benjamin, Ryder Nick, Subbiah Melanie, Kaplan Jared, Dhariwal Prafulla, Neelakantan Arvind, Shyam Pranav, Sastry Girish, Askell Amanda, et\u00a0al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020."},{"issue":"9","key":"796_CR8","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"Hongyun Cai","year":"2018","unstructured":"Cai Hongyun, Zheng Vincent\u00a0W, Chen-Chuan Chang Kevin. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng. 2018;30(9):1616\u201337.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"796_CR9","doi-asserted-by":"crossref","unstructured":"Carter Daniel. Hustle and brand: The sociotechnical shaping of influence. Social Media+ Society, 2016;2(3):2056305116666305.","DOI":"10.1177\/2056305116666305"},{"key":"796_CR10","doi-asserted-by":"crossref","unstructured":"Cha Meeyoung, Haddadi Hamed, Benevenuto Fabricio, Gummadi Krishna. Measuring user influence in twitter: The million follower fallacy. In Proceedings of the International AAAI Conference on Web and Social Media, 2010;volume\u00a04.","DOI":"10.1609\/icwsm.v4i1.14033"},{"key":"796_CR11","doi-asserted-by":"crossref","unstructured":"Chavoshi Nikan, Hamooni Hossein, Mueen Abdullah. Debot: Twitter bot detection via warped correlation. In Icdm, 2016;pages 817\u2013822.","DOI":"10.1109\/ICDM.2016.0096"},{"issue":"1","key":"796_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"Manlio De\u00a0Domenico","year":"2020","unstructured":"De\u00a0Domenico Manlio, Altmann Eduardo\u00a0G. Unraveling the origin of social bursts in collective attention. Sci Rep. 2020;10(1):1\u20139.","journal-title":"Sci Rep"},{"key":"796_CR13","unstructured":"Dong Guozhu, Liu Huan. Feature engineering for machine learning and data analytics. CRC Press, 2018."},{"key":"796_CR14","doi-asserted-by":"crossref","unstructured":"Donnat Claire, Zitnik Marinka, Hallac David, Leskovec Jure. Learning structural node embeddings via diffusion wavelets. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018;pages 1320\u20131329.","DOI":"10.1145\/3219819.3220025"},{"key":"796_CR15","doi-asserted-by":"crossref","unstructured":"Feng S, Wan H, Wang N, Li J, Luo M. Twibot-20: A comprehensive twitter bot detection benchmark. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021;1:4485\u20134494.","DOI":"10.1145\/3459637.3482019"},{"key":"796_CR16","first-page":"894439320914853","volume":"1","author":"Freelon Deen","year":"2020","unstructured":"Deen Freelon, Michael Bossetta, Chris Wells, Josephine Lukito, Yiping Xia, Kirsten Adams. Black trolls matter: racial and ideological asymmetries in social media disinformation. Soc Sci Comput Rev. 2020;1:894439320914853.","journal-title":"Soc Sci Comput Rev"},{"key":"796_CR17","doi-asserted-by":"crossref","unstructured":"Freitas Carlos, Benevenuto Fabricio, Ghosh Saptarshi, Veloso Adriano. Reverse engineering socialbot infiltration strategies in twitter. In 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 25\u201332. IEEE, 2015.","DOI":"10.1145\/2808797.2809292"},{"key":"796_CR18","first-page":"1","volume":"12","author":"Hongyu Gao","year":"2012","unstructured":"Gao Hongyu, Chen Yan, Lee Kathy, Palsetia Diana, Choudhary Alok\u00a0N. Towards online spam filtering in social networks. NDSS. 2012;12:1\u201316.","journal-title":"NDSS"},{"key":"796_CR19","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"Palash Goyal","year":"2018","unstructured":"Goyal Palash, Ferrara Emilio. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Syst. 2018;151:78\u201394.","journal-title":"Knowledge-Based Syst"},{"key":"796_CR20","unstructured":"Grootendorst Maarten. Bertopic: Leveraging bert and c-tf-idf to create easily interpretable topics., 2020."},{"key":"796_CR21","doi-asserted-by":"crossref","unstructured":"Grover Aditya, Leskovec Jure. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016;pp. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"796_CR22","doi-asserted-by":"crossref","unstructured":"Hamdi Tarek, Slimi Hamda, Bounhas Ibrahim, Slimani Yahya. A hybrid approach for fake news detection in twitter based on user features and graph embedding. In Distributed Computing and Internet Technology: 16th International Conference, ICDCIT 2020, Bhubaneswar, India, January 9\u201312, 2020, Proceedings 16, 2020;p. 266\u2013280. Springer.","DOI":"10.1007\/978-3-030-36987-3_17"},{"key":"796_CR23","doi-asserted-by":"crossref","unstructured":"Henderson Keith, Gallagher Brian, Eliassi-Rad Tina, Tong Hanghang, Basu Sugato, Akoglu Leman, Koutra Danai, Faloutsos Christos, Li Lei. Rolx: structural role extraction & mining in large graphs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012;pages 1231\u20131239.","DOI":"10.1145\/2339530.2339723"},{"issue":"2","key":"796_CR24","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1145\/2090150.2090161","volume":"19","author":"Tim Hwang","year":"2012","unstructured":"Hwang Tim, Pearce Ian, Nanis Max. Socialbots: Voices from the fronts. interactions. 2012;19(2):38\u201345.","journal-title":"interactions"},{"key":"796_CR25","doi-asserted-by":"crossref","unstructured":"Kami\u0144ski Bogumi\u0142, Pra\u0142at Pawe\u0142, Th\u00e9berge Fran\u00e7ois. Mining Complex Networks. CRC Press, 2021.","DOI":"10.1201\/9781003218869"},{"key":"796_CR26","doi-asserted-by":"crossref","unstructured":"Lee Kyumin, Eoff Brian, Caverlee James. Seven months with the devils: A long-term study of content polluters on twitter. In: Proceedings of the international AAAI conference on web and social media. 2011;5:185\u201392.","DOI":"10.1609\/icwsm.v5i1.14106"},{"issue":"3","key":"796_CR27","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/TDSC.2013.3","volume":"10","author":"Sangho Lee","year":"2013","unstructured":"Lee Sangho, Kim Jong. Warningbird: A near real-time detection system for suspicious urls in twitter stream. IEEE transactions on dependable and secure computing. 2013;10(3):183\u201395.","journal-title":"IEEE transactions on dependable and secure computing"},{"key":"796_CR28","doi-asserted-by":"crossref","unstructured":"Lehmann Janette, Gon\u00e7alves Bruno, Ramasco Jos\u00e9\u00a0J, Cattuto Ciro. Dynamical classes of collective attention in twitter. In Proceedings of the 21st international conference on World Wide Web, 2012;p. 251\u2013260.","DOI":"10.1145\/2187836.2187871"},{"key":"796_CR29","doi-asserted-by":"crossref","unstructured":"Magelinski Thomas, Beskow David, Carley Kathleen\u00a0M. Graph-hist: Graph classification from latent feature histograms with application to bot detection. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020;34:5134\u20135141.","DOI":"10.1609\/aaai.v34i04.5956"},{"key":"796_CR30","doi-asserted-by":"crossref","unstructured":"Matwin Stan, Milios Aristides, Pra\u0142at Pawe\u0142, Soares Amilcar, Th\u00e9berge Fran\u00e7ois. Generative methods for social media analysis. SpringerBriefs in Computer Science, 2023.","DOI":"10.1007\/978-3-031-33617-1"},{"issue":"1","key":"796_CR31","doi-asserted-by":"crossref","first-page":"015","DOI":"10.1093\/cybsec\/tyac015","volume":"9","author":"I Mbona","year":"2023","unstructured":"Mbona I, Eloff-Jan HP. Classifying social media bots as malicious or benign using semi-supervised machine learning. J Cybersec. 2023;9(1):015.","journal-title":"J Cybersec"},{"key":"796_CR32","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado-Greg S, Dean J. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, 2013;1:3111\u20133119."},{"key":"796_CR33","doi-asserted-by":"crossref","unstructured":"Minnich Amanda, Chavoshi Nikan, Koutra Danai, Mueen Abdullah. Botwalk: Efficient adaptive exploration of twitter bot networks. In Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining 2017, 2017;pages 467\u2013474.","DOI":"10.1145\/3110025.3110163"},{"key":"796_CR34","unstructured":"Monti Federico, Frasca Fabrizio, Eynard Davide, Mannion Damon, Bronstein Michael\u00a0M. Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673, 2019."},{"key":"796_CR35","unstructured":"OpenAI. Gpt-4 technical report, 2023."},{"issue":"1","key":"796_CR36","doi-asserted-by":"publisher","first-page":"19","DOI":"10.21609\/jiki.v8i1.280","volume":"8","author":"Rizal\u00a0Setya Perdana","year":"2015","unstructured":"Perdana Rizal\u00a0Setya, Muliawati Tri\u00a0Hadiah, Alexandro Reddy. Bot spammer detection in twitter using tweet similarity and time interval entropy. Jurnal Ilmu Komputer dan Informasi. 2015;8(1):19\u201325.","journal-title":"Jurnal Ilmu Komputer dan Informasi"},{"key":"796_CR37","doi-asserted-by":"crossref","unstructured":"Perozzi Bryan, Al-Rfou Rami, Skiena Steven. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014;p. 701\u2013710.","DOI":"10.1145\/2623330.2623732"},{"key":"796_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101771","volume":"103","author":"Phu Pham","year":"2022","unstructured":"Pham Phu, Nguyen Loan\u00a0TT, Vo Bay, Yun Unil. Bot2vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks. Inform Syst. 2022;103: 101771.","journal-title":"Inform Syst"},{"key":"796_CR39","unstructured":"Ribeiro Leonardo\u00a0FR, Saverese Pedro\u00a0HP, Figueiredo Daniel\u00a0R. struc2vec: Learning node representations from structural identity. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017;385\u2013394."},{"issue":"4","key":"796_CR40","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/TKDE.2014.2349913","volume":"27","author":"Ryan\u00a0A Rossi","year":"2014","unstructured":"Rossi Ryan\u00a0A, Ahmed Nesreen\u00a0K. Role discovery in networks. IEEE Trans Knowl Data Eng. 2014;27(4):1112\u201331.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"(4","key":"796_CR41","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1207\/S15327957PSPR0504_2","volume":"5","author":"Paul Rozin","year":"2001","unstructured":"Rozin Paul, Royzman Edward\u00a0B. Negativity bias, negativity dominance, and contagion. Person Soc Psychol Rev. 2001;5((4):296\u2013320.","journal-title":"Person Soc Psychol Rev"},{"key":"796_CR42","doi-asserted-by":"crossref","unstructured":"Sayyadiharikandeh Mohsen, Varol Onur, Yang Kai-Cheng, Flammini Alessandro, Menczer Filippo. Detection of novel social bots by ensembles of specialized classifiers. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020;pages 2725\u20132732.","DOI":"10.1145\/3340531.3412698"},{"issue":"5","key":"796_CR43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214210","volume":"14","author":"Massimo Stella","year":"2019","unstructured":"Stella Massimo, Cristoforetti Marco, De Domenico Manlio. Influence of augmented humans in online interactions during voting events. PLoS ONE. 2019;14(5): e0214210.","journal-title":"PLoS ONE"},{"key":"796_CR44","unstructured":"Tan Zhaoxuan, Feng Shangbin, Sclar Melanie, Wan Herun, Luo Minnan, Choi Yejin, Tsvetkov Yulia. Botpercent: Estimating twitter bot populations from groups to crowds. arXiv preprint arXiv:2302.00381, 2023."},{"key":"796_CR45","doi-asserted-by":"crossref","unstructured":"Thomas Kurt, Grier Chris, Ma Justin, Paxson Vern, Song Dawn. Design and evaluation of a real-time url spam filtering service. In 2011 IEEE symposium on security and privacy, pages 447\u2013462. IEEE, 2011.","DOI":"10.1109\/SP.2011.25"},{"key":"796_CR46","doi-asserted-by":"crossref","unstructured":"Wolf Thomas, Debut Lysandre, Sanh Victor, Chaumond Julien, Delangue Clement, Moi Anthony, Cistac Pierric, Rault Tim, Louf R\u00e9mi, Funtowicz Morgan, Davison Joe, Shleifer Sam, von Platen Patrick, Ma Clara, Jernite Yacine, Plu Julien, Xu Canwen, Scao Teven\u00a0Le, Gugger Sylvain, Drame Mariama, Lhoest Quentin, Rush Alexander\u00a0M. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38\u201345, Online, October 2020. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"796_CR47","unstructured":"Woolley Samuel\u00a0C, Howard Philip\u00a0N. Computational propaganda: political parties, politicians, and political manipulation on social media. Oxford University Press, 2018."},{"issue":"1","key":"796_CR48","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1002\/hbe2.115","volume":"1","author":"Kai-Cheng Yang","year":"2019","unstructured":"Yang Kai-Cheng, Varol Onur, Davis Clayton\u00a0A, Ferrara Emilio, Flammini Alessandro, Menczer Filippo. Arming the public with artificial intelligence to counter social bots. Human Beh Emerg Technol. 2019;1(1):48\u201361.","journal-title":"Human Beh Emerg Technol"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00796-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00796-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00796-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T04:42:20Z","timestamp":1702788140000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00796-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,19]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["796"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00796-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1428343\/v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,19]]},"assertion":[{"value":"7 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","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":"119"}}