{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:16:19Z","timestamp":1768680979269,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the rise of Web 2.0 platforms such as online social media, people\u2019s private information, such as their location, occupation and even family information, is often inadvertently disclosed through online discussions. Therefore, it is important to detect such unwanted privacy disclosures to help alert people affected and the online platform. In this paper, privacy disclosure detection is modeled as a multi-label text classification (MLTC) problem, and a new privacy disclosure detection model is proposed to construct an MLTC classifier for detecting online privacy disclosures. This classifier takes an online post as the input and outputs multiple labels, each reflecting a possible privacy disclosure. The proposed presentation method combines three different sources of information, the input text itself, the label-to-text correlation and the label-to-label correlation. A double-attention mechanism is used to combine the first two sources of information, and a graph convolutional network is employed to extract the third source of information that is then used to help fuse features extracted from the first two sources of information. Our extensive experimental results, obtained on a public dataset of privacy-disclosing posts on Twitter, demonstrated that our proposed privacy disclosure detection method significantly and consistently outperformed other state-of-the-art methods in terms of all key performance indicators.<\/jats:p>","DOI":"10.1007\/s10618-023-00992-y","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T07:02:41Z","timestamp":1704438161000},"page":"1171-1192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8427-2086","authenticated-orcid":false,"given":"Zhanbo","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"992_CR1","doi-asserted-by":"publisher","unstructured":"Adhikari A, Ram A, Tang R, et al. (2019) DocBERT: BERT for document classification. arXiv:1904.08398 [cs.CL]. https:\/\/doi.org\/10.48550\/arXiv.1904.08398","DOI":"10.48550\/arXiv.1904.08398"},{"key":"992_CR2","doi-asserted-by":"publisher","unstructured":"Biega AJ, Roy RS, Weikum G (2017) Privacy through solidarity: A user-utility-preserving framework to counter profiling. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 675\u2013684. https:\/\/doi.org\/10.1145\/3077136.3080830","DOI":"10.1145\/3077136.3080830"},{"key":"992_CR3","doi-asserted-by":"publisher","unstructured":"Chen G, Ye D, Xing Z, et al. (2017) Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: Proceedings of the 2017 international joint conference on neural networks. IEEE, pp 2377\u20132383. https:\/\/doi.org\/10.1109\/IJCNN.2017.7966144","DOI":"10.1109\/IJCNN.2017.7966144"},{"issue":"4","key":"992_CR4","doi-asserted-by":"publisher","first-page":"37:1","DOI":"10.1145\/3406109","volume":"38","author":"X Chen","year":"2020","unstructured":"Chen X, Song X, Ren R et al. (2020) Fine-grained privacy detection with graph-regularized hierarchical attentive representation learning. ACM Trans Inf Syst 38(4):37:1-37:26. https:\/\/doi.org\/10.1145\/3406109","journal-title":"ACM Trans Inf Syst"},{"key":"992_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"key":"992_CR6","doi-asserted-by":"publisher","unstructured":"Eslami S, Biega AJ, Saha\u00a0Roy R, et al. (2017) Privacy of hidden profiles: Utility-preserving profile removal in online forums. In: Proceedings of the 2017 ACM conference on information and knowledge management. ACM, pp 2063\u20132066. https:\/\/doi.org\/10.1145\/3132847.3133140","DOI":"10.1145\/3132847.3133140"},{"issue":"2","key":"992_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TNN.1994.8753425","volume":"5","author":"CL Giles","year":"1994","unstructured":"Giles CL, Kuhn GM, Williams RJ (1994) Dynamic recurrent neural networks: theory and applications. IEEE Trans Neural Netw 5(2):153\u2013156. https:\/\/doi.org\/10.1109\/TNN.1994.8753425","journal-title":"IEEE Trans Neural Netw"},{"key":"992_CR8","doi-asserted-by":"publisher","unstructured":"Huang X, Paul MJ (2019) Neural user factor adaptation for text classification: Learning to generalize across author demographics. In: Proceedings of the 8th joint conference on lexical and computational semantics. ACL, pp 136\u2013146. https:\/\/doi.org\/10.18653\/v1\/S19-1015","DOI":"10.18653\/v1\/S19-1015"},{"key":"992_CR9","unstructured":"Jacob L, Vert JP, Bach F (2008) Clustered multi-task learning: a convex formulation. In: Proceedings of the 22nd annual conference on neural information processing systems. Curran Associates, Inc., pp 745\u2013752. https:\/\/proceedings.neurips.cc\/paper\/2008\/hash\/fccb3cdc9acc14a6e70a12f74560c026-Abstract.html"},{"key":"992_CR10","doi-asserted-by":"publisher","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing. ACL, pp 1746\u20131751. https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"992_CR11","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference for learning representations. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"992_CR12","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations. OpenReview. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"992_CR13","unstructured":"Kumar A, Daum\u00e9\u00a0III H (2012) Learning task grouping and overlap in multi-task learning. In: Proceedings of the 29th international conference on machine learning. ICML. https:\/\/icml.cc\/2012\/papers\/690.pdf"},{"key":"992_CR14","doi-asserted-by":"publisher","unstructured":"Kurata G, Bing X, Zhou B (2016) Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. ACL, pp 521\u2013526. https:\/\/doi.org\/10.18653\/v1\/N16-1063","DOI":"10.18653\/v1\/N16-1063"},{"key":"992_CR15","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on machine learning. PMLR, pp 1188\u20131196. https:\/\/proceedings.mlr.press\/v32\/le14.html"},{"key":"992_CR16","unstructured":"Lin Z, Feng M, Santos CNd, et al. (2017) A structured self-attentive sentence embedding. In: Proceedings of the 5th international conference on learning representations. OpenReview. https:\/\/openreview.net\/forum?id=BJC_jUqxe"},{"key":"992_CR17","doi-asserted-by":"publisher","unstructured":"Liu J, Chang WC, Wu Y, et al. (2017) Deep learning for extreme multi-label text classification. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 115\u2013124. https:\/\/doi.org\/10.1145\/3077136.3080834","DOI":"10.1145\/3077136.3080834"},{"key":"992_CR18","unstructured":"Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. In: Proceedings of the 25th international joint conference on artificial intelligence. IJCAI, pp 2873\u20132879. https:\/\/www.ijcai.org\/Proceedings\/16\/Papers\/408.pdf"},{"key":"992_CR19","doi-asserted-by":"publisher","unstructured":"Ma Q, Yuan C, Zhou W, et al. (2021) Label-specific dual graph neural network for multi-label text classification. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing. ACL, pp 3855\u20133864. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.298","DOI":"10.18653\/v1\/2021.acl-long.298"},{"key":"992_CR20","doi-asserted-by":"publisher","unstructured":"Mao H, Shuai X, Kapadia A (2011) Loose tweets: An analysis of privacy leaks on Twitter. In: Proceedings of the 10th annual ACM workshop on privacy in the electronic society. ACM, pp 1\u201312. https:\/\/doi.org\/10.1145\/2046556.2046558","DOI":"10.1145\/2046556.2046558"},{"key":"992_CR21","unstructured":"Nguyen C, Zhan D, Zhou Z (2013) Multi-modal image annotation with multi-instance multi-label LDA. In: Proceedings of the 23rd international joint conference on artificial intelligence. AAAI, pp 1558\u20131564. https:\/\/www.ijcai.org\/Proceedings\/13\/Papers\/232.pdf"},{"key":"992_CR22","doi-asserted-by":"publisher","unstructured":"Raber F, Kr\u00fcger A (2018) Deriving privacy settings for location sharing: Are context factors always the best choice? In: Proceedings of the 2018 IEEE symposium on privacy-aware computing. IEEE, pp 86\u201394. https:\/\/doi.org\/10.1109\/PAC.2018.00015","DOI":"10.1109\/PAC.2018.00015"},{"key":"992_CR23","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s11257-019-09246-3","volume":"30","author":"OR Sanchez","year":"2020","unstructured":"Sanchez OR, Torre I, He Y et al. (2020) A recommendation approach for user privacy preferences in the fitness domain. User Model User-Adap Inter 30:513\u2013565. https:\/\/doi.org\/10.1007\/s11257-019-09246-3","journal-title":"User Model User-Adap Inter"},{"key":"992_CR24","doi-asserted-by":"publisher","unstructured":"Song X, Wang X, Nie L, et al. (2018) A personal privacy preserving framework: I let you know who can see what. In: Proceedings of the 41st international ACM SIGIR conference on research & development in information retrieval. ACM, pp 295\u2013304. https:\/\/doi.org\/10.1145\/3209978.3209995","DOI":"10.1145\/3209978.3209995"},{"key":"992_CR25","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58:267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"key":"992_CR26","doi-asserted-by":"publisher","unstructured":"Tran L, Kong D, Jin H, et al. (2016) Privacy-CNH: A framework to detect photo privacy with convolutional neural network using hierarchical features. In: Proceedings of the 30th AAAI conference on artificial intelligence. AAAI, pp 1317\u20131323. https:\/\/doi.org\/10.1609\/aaai.v30i1.10169","DOI":"10.1609\/aaai.v30i1.10169"},{"key":"992_CR27","doi-asserted-by":"publisher","unstructured":"Xiao L, Huang X, Chen B, et al. (2019) Label-specific document representation for multi-label text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing. ACL, pp 466\u2013475. https:\/\/doi.org\/10.18653\/v1\/D19-1044","DOI":"10.18653\/v1\/D19-1044"},{"key":"992_CR28","unstructured":"Yang P, Sun X, Li W, et al. (2018) SGM: Sequence generation model for multi-label classification. In: Proceedings of the 27th international conference on computational linguistics. ICCL, pp 3915\u20133926. https:\/\/aclanthology.org\/C18-1330"},{"key":"992_CR29","unstructured":"Zhang M, Zhou Z (2007) Multi-label learning by instance differentiation. In: Proceedings of the 2007 AAAI conference on artificial intelligence, vol\u00a07. AAAI, pp 669\u2013674. https:\/\/aaai.org\/papers\/00669-multi-label-learning-by-instance-differentiation\/"},{"key":"992_CR30","doi-asserted-by":"publisher","unstructured":"Zhou P, Qi Z, Zheng S, et al. (2016) Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv:1611.06639 [cs.CL]. https:\/\/doi.org\/10.48550\/arXiv.1611.06639","DOI":"10.48550\/arXiv.1611.06639"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00992-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-023-00992-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-023-00992-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T09:14:51Z","timestamp":1714814091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-023-00992-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,5]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["992"],"URL":"https:\/\/doi.org\/10.1007\/s10618-023-00992-y","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,5]]},"assertion":[{"value":"28 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}