{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:47:43Z","timestamp":1771915663214,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s12652-020-02640-5","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T14:05:36Z","timestamp":1604930736000},"page":"9287-9302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["TextSpamDetector: textual content based deep learning framework for social spam detection using conjoint attention mechanism"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6175-762X","authenticated-orcid":false,"given":"E.","family":"Elakkiya","sequence":"first","affiliation":[]},{"given":"S.","family":"Selvakumar","sequence":"additional","affiliation":[]},{"given":"R.","family":"Leela Velusamy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"2640_CR1","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1016\/j.comcom.2013.04.004","volume":"3610\u201311","author":"F Ahmed","year":"2013","unstructured":"Ahmed F, Abulaish M (2013) A generic statistical approach for spam detection in online social networks. Comput Commun 3610\u201311:1120\u20131129","journal-title":"Comput Commun"},{"key":"2640_CR2","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.knosys.2018.04.025","volume":"153","author":"AZ Ala\u2019M","year":"2018","unstructured":"Ala\u2019M AZ et al (2018) Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl Based Syst 153:91\u2013104","journal-title":"Knowl Based Syst"},{"key":"2640_CR3","doi-asserted-by":"crossref","unstructured":"Alghamdi B, Watson J, Xu Y (2016) Toward detecting malicious links in online social networks through user behavior. In: 2016 IEEE\/WIC\/ACM international conference on web intelligence workshops (WIW)","DOI":"10.1109\/WIW.2016.014"},{"key":"2640_CR4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.knosys.2016.05.001","volume":"108","author":"TA Almeida","year":"2016","unstructured":"Almeida TA et al (2016) Text normalization and semantic indexing to enhance instant messaging and SMS spam filtering. Knowl Based Syst 108:25\u201332","journal-title":"Knowl Based Syst"},{"key":"2640_CR5","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"key":"2640_CR6","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.artmed.2018.11.004","volume":"97","author":"I Banerjee","year":"2019","unstructured":"Banerjee I et al (2019) Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med 97:79\u201388","journal-title":"Artif Intell Med"},{"issue":"10","key":"2640_CR7","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1007\/s10489-018-1161-y","volume":"48","author":"A Barushka","year":"2018","unstructured":"Barushka A, Hajek P (2018) Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks. Appl Intell 48(10):3538\u20133556","journal-title":"Appl Intell"},{"key":"2640_CR8","unstructured":"Benevenuto F et al. (2010) Detecting spammers on twitter. In: Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), vol 6"},{"key":"2640_CR9","doi-asserted-by":"crossref","unstructured":"Cao C, Caverlee J (2014) Behavioral detection of spam URL sharing: posting patterns versus click patterns. In: 2014 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM 2014)","DOI":"10.1109\/ASONAM.2014.6921573"},{"issue":"3","key":"2640_CR10","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TCSS.2016.2516039","volume":"2","author":"C Chen","year":"2015","unstructured":"Chen C et al (2015) A performance evaluation of machine learning-based streaming spam tweets detection. IEEE Trans Comput Soc Syst 2(3):65\u201376","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"4","key":"2640_CR11","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TIFS.2016.2621888","volume":"12","author":"C Chen","year":"2016","unstructured":"Chen C et al (2016) Statistical features-based real-time detection of drifted twitter spam. IEEE Trans Inf Forensics Secur 12(4):914\u2013925","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2640_CR12","doi-asserted-by":"crossref","unstructured":"Cheng Z, Bai F, Xu Y, Zheng G, Pu S, Zhou S (2017) Focusing attention: towards accurate text recognition in natural images. In Proceedings of the IEEE international conference on computer vision, pp 5076\u20135084","DOI":"10.1109\/ICCV.2017.543"},{"key":"2640_CR13","doi-asserted-by":"crossref","unstructured":"Conneau A et al. (2017) Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364","DOI":"10.18653\/v1\/D17-1070"},{"key":"2640_CR14","unstructured":"Egele M et al. (2015) Towards detecting compromised accounts on social networks. IEEE"},{"issue":"4","key":"2640_CR15","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MNET.2018.1700406","volume":"32","author":"B Feng","year":"2018","unstructured":"Feng B et al (2018) Multistage and elastic spam detection in mobile social networks through deep learning. IEEE Network 32(4):15\u201321","journal-title":"IEEE Network"},{"issue":"1","key":"2640_CR16","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11280-018-0529-6","volume":"22","author":"S Feng","year":"2019","unstructured":"Feng S, Wang Y, Liu L, Wang D, Yu G (2019) Attention based hierarchical LSTM network for context-aware microblog sentiment classification. World Wide Web 22(1):59\u201381","journal-title":"World Wide Web"},{"issue":"1","key":"2640_CR17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10472-018-9612-z","volume":"85","author":"G Jain","year":"2019","unstructured":"Jain G, Sharma M, Agarwal B (2019) Spam detection in social media using convolutional and long short term memory neural network. Ann Math Artif Intell 85(1):21\u201344","journal-title":"Ann Math Artif Intell"},{"key":"2640_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01541-6","author":"T Jose","year":"2019","unstructured":"Jose T, Babu SS (2019) Detecting spammers on social network through clustering technique. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-019-01541-6","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2640_CR19","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1746\u20131751, Doha, Qatar, Association for Computational Linguistics","DOI":"10.3115\/v1\/D14-1181"},{"key":"2640_CR20","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.cose.2016.12.004","volume":"69","author":"S Liu","year":"2017","unstructured":"Liu S et al (2017) Addressing the class imbalance problem in twitter spam detection using ensemble learning. Comput Secur 69:35\u201349","journal-title":"Comput Secur"},{"key":"2640_CR21","doi-asserted-by":"crossref","unstructured":"Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","DOI":"10.18653\/v1\/D15-1166"},{"issue":"4","key":"2640_CR22","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TCSS.2018.2878852","volume":"5","author":"S Madisetty","year":"2018","unstructured":"Madisetty S, Desarkar MS (2018) A neural network-based ensemble approach for spam detection in Twitter. IEEE Trans Comput Soc Syst 5(4):973\u2013984","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"8","key":"2640_CR23","doi-asserted-by":"publisher","first-page":"2992","DOI":"10.1016\/j.eswa.2012.12.015","volume":"40","author":"J Martinez-Romo","year":"2013","unstructured":"Martinez-Romo J, Araujo L (2013) Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Syst Appl 40(8):2992\u20133000","journal-title":"Expert Syst Appl"},{"key":"2640_CR24","unstructured":"Mikolov T et al. (2013a) Efficient estimation of word representations in vector space. In: Proceeding of workshop at first international conference on learning representation (ICLR)"},{"key":"2640_CR25","unstructured":"Mikolov T, Yih W, Zweig G (2013b) 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"},{"key":"2640_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2018.04.045","volume":"308","author":"G Rao","year":"2018","unstructured":"Rao G et al (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49\u201357","journal-title":"Neurocomputing"},{"key":"2640_CR28","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1016\/j.asoc.2017.09.032","volume":"67","author":"S Rathore","year":"2018","unstructured":"Rathore S, Loia V, Park JH (2018) SpamSpotter: an efficient spammer detection framework based on intelligent decision support system on facebook. Appl Soft Comput 67:920\u2013932","journal-title":"Appl Soft Comput"},{"key":"2640_CR29","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.04.025","volume":"116","author":"M Sar\u0131g\u00fcl","year":"2019","unstructured":"Sar\u0131g\u00fcl M, Ozyildirim BM, Avci M (2019) Differential convolutional neural network. Neural Networks 116:279\u2013287","journal-title":"Neural Networks"},{"key":"2640_CR30","doi-asserted-by":"crossref","unstructured":"Sedhai S, Sun A (2015) Hspam14: a collection of 14 million tweets for hashtag-oriented spam research. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval.","DOI":"10.1145\/2766462.2767701"},{"issue":"1","key":"2640_CR31","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/TCSS.2017.2773581","volume":"5","author":"S Sedhai","year":"2017","unstructured":"Sedhai S, Sun A (2017) Semi-supervised spam detection in Twitter stream. IEEE Trans Comput Soc Syst 5(1):169\u2013175","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"7","key":"2640_CR32","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/TIFS.2017.2675361","volume":"12","author":"S Shehnepoor","year":"2017","unstructured":"Shehnepoor S et al (2017) NetSpam: a network-based spam detection framework for reviews in online social media. IEEE Trans Inf Forensics Secur 12(7):1585\u20131595","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2640_CR33","unstructured":"Simon K (2020) Digital 2020: 3.8 billion people use social Media. We Are Social Inc. https:\/\/wearesocial.com\/blog\/2020\/01\/digital-2020-3-8-billion-people-use-social-media. Accessed 20 Feb 2020"},{"issue":"1","key":"2640_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s10660-016-9244-5","volume":"17","author":"L Song","year":"2017","unstructured":"Song L et al (2017) Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection. Electron Commer Res 17(1):51\u201381","journal-title":"Electron Commer Res"},{"key":"2640_CR35","volume-title":"A thesis on A corpus linguistics study of SMS text messaging","author":"C Tagg","year":"2009","unstructured":"Tagg C (2009) A thesis on A corpus linguistics study of SMS text messaging. University of Birmingham, Diss"},{"issue":"4","key":"2640_CR36","first-page":"447","volume":"14","author":"K Thomas","year":"2011","unstructured":"Thomas K et al (2011) Design and evaluation of a real-time URL spam filtering service. 2011 IEEE symposium on security and privacy. Trans Dependable Secure Comput 14(4):447\u2013460","journal-title":"Trans Dependable Secure Comput"},{"key":"2640_CR26","unstructured":"UtkMl's Twitter Spam Detection Competition (2019).https:\/\/www.kaggle.com\/c\/twitter-spam\/data. Accessed Nov 2019"},{"key":"2640_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"issue":"19","key":"2640_CR39","doi-asserted-by":"publisher","first-page":"e4209","DOI":"10.1002\/cpe.4209","volume":"29","author":"T Wu","year":"2017","unstructured":"Wu T et al (2017a) Detecting spamming activities in twitter based on deep-learning technique. Concurr Comput Pract Exp 29(19):e4209","journal-title":"Concurr Comput Pract Exp"},{"key":"2640_CR38","doi-asserted-by":"crossref","unstructured":"Wu T et al (2017b) Twitter spam detection based on deep learning. In: Proceedings of the australasian computer science week multiconference","DOI":"10.1145\/3014812.3014815"},{"key":"2640_CR40","doi-asserted-by":"publisher","first-page":"51522","DOI":"10.1109\/ACCESS.2019.2909919","volume":"7","author":"G Xu","year":"2019","unstructured":"Xu G et al (2019a) Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7:51522\u201351532","journal-title":"IEEE Access"},{"key":"2640_CR41","first-page":"10067","volume":"33","author":"J Xu","year":"2019","unstructured":"Xu J et al (2019b) Incorporating context-relevant concepts into convolutional neural networks for short text classification. Neurocomputing. 33:10067\u201310068","journal-title":"Neurocomputing."},{"issue":"8","key":"2640_CR42","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1109\/TIFS.2013.2267732","volume":"8","author":"C Yang","year":"2013","unstructured":"Yang C, Harkreader R, Guofei Gu (2013) Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Trans Inf Forensics Secur 8(8):1280\u20131293","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"1","key":"2640_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2846102","volume":"10","author":"X Zhang","year":"2016","unstructured":"Zhang X et al (2016) Detecting spam and promoting campaigns in Twitter. ACM Trans Web (TWEB) 10(1):1\u201328","journal-title":"ACM Trans Web (TWEB)"},{"key":"2640_CR44","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.02.047","volume":"159","author":"X Zheng","year":"2015","unstructured":"Zheng X et al (2015) Detecting spammers on social networks. Neurocomputing 159:27\u201334","journal-title":"Neurocomputing"},{"key":"2640_CR45","unstructured":"Zhou C et al (2015) A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630"},{"key":"2640_CR46","doi-asserted-by":"crossref","unstructured":"Zhou Y, Xu B, Xu J, Yang L, Li C (2016) Compositional recurrent neural networks for chinese short text classification. In: 2016 IEEE\/WIC\/ACM international conference on web intelligence (WI), pp. 137\u2013144.","DOI":"10.1109\/WI.2016.0029"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02640-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-020-02640-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02640-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T19:42:32Z","timestamp":1630438952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-020-02640-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":46,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["2640"],"URL":"https:\/\/doi.org\/10.1007\/s12652-020-02640-5","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]},"assertion":[{"value":"8 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}