{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:12:59Z","timestamp":1760058779288,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T00:00:00Z","timestamp":1745712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Jilin Province","award":["20220101117JC"],"award-info":[{"award-number":["20220101117JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this study, we propose a computational approach that applies text mining and deep learning to conduct controversy detection on social media platforms. Unlike previous research, our method integrates multidimensional and heterogeneous information from social media into a heterogeneous signed attributed network, encompassing various users\u2019 attributes, semantic information, and structural heterogeneity. We introduce a deep dual-layer self-supervised algorithm for community detection and analyze controversy within this network. A novel controversy metric is devised by considering three dimensions of controversy: community distinctions, betweenness centrality, and user representations. A comparison between our method and other classical controversy measures such as Random Walk, Biased Random Walk (BRW), BCC, EC, GMCK, MBLB, and community-based methods reveals that our model consistently produces more stable and accurate controversy scores. Additionally, we calculated the level of controversy and computed p-values for the detected communities on our crawled dataset Weibo, including #Microblog (3792), #Comment (45,741), #Retweet (36,126), and #User (61,327). Overall, our model had a comprehensive and nuanced understanding of controversy on social media platforms. To facilitate its use, we have developed a user-friendly web server.<\/jats:p>","DOI":"10.3390\/e27050473","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T09:00:39Z","timestamp":1745917239000},"page":"473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Controversy Detection Based on Heterogeneous Signed Attributed Network and Deep Dual-Layer Self-Supervised Community Analysis"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7804-149X","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0749-5811","authenticated-orcid":false,"given":"Qianqian","family":"Li","sequence":"additional","affiliation":[{"name":"Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9216","DOI":"10.1073\/pnas.1804840115","article-title":"Exposure to Opposing Views on Social Media Can Increase Political Polarization","volume":"115","author":"Bail","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","unstructured":"Isaac, M., and Shane, S. (New York Times, 2017). Facebook\u2019s Russia-Linked Ads Came in Many Disguises, New York Times."},{"key":"ref_3","unstructured":"Guerra, P., Meira, W., Cardie, C., and Kleinberg, R. (2013, January 8\u201311). A Measure of Polarization on Social Media Networks Based on Community Boundaries. Proceedings of the International AAAI Conference on Web and Social Media, Cambridge, MA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.knosys.2014.05.008","article-title":"V PoliTwi: Early Detection of Emerging Political Topics on Twitter and the Impact on Concept-Level Sentiment Analysis","volume":"69","author":"Rill","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/aris.1440380105","article-title":"Latent Semantic Analysis","volume":"38","author":"Dumais","year":"2004","journal-title":"Annu. Rev. Inf. Sci. Technol."},{"key":"ref_6","unstructured":"Garimella, K. (2018). Polarization on Social Media, Aalto University."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.ins.2019.03.041","article-title":"Investigating the Opinions Distribution in the Controversy on Social Media","volume":"489","author":"Qiu","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hessel, J., and Lee, L. (2019). Something\u2019s Brewing! Early Prediction of Controversy-Causing Posts from Discussion Features. arXiv.","DOI":"10.18653\/v1\/N19-1166"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ins.2019.06.060","article-title":"Opinion Community Detection and Opinion Leader Detection Based on Text Information and Network Topology in Cloud Environment","volume":"504","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Al Amin, M.T., Aggarwal, C., Yao, S., Abdelzaher, T., and Kaplan, L. (2017, January 1\u20134). Unveiling Polarization in Social Networks: A Matrix Factorization Approach. Proceedings of the IEEE INFOCOM 2017-IEEE Conference on Computer Communications, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8056959"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102366","DOI":"10.1016\/j.ipm.2020.102366","article-title":"GENE: Graph Generation Conditioned on Named Entities for Polarity and Controversy Detection in Social Media","volume":"57","author":"Mendoza","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhong, L., Cao, J., Sheng, Q., Guo, J., and Wang, Z. (2020, January 5\u201310). Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.49"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1080\/0022250X.2016.1147443","article-title":"Disambiguation of Social Polarization Concepts and Measures","volume":"40","author":"Bramsona","year":"2016","journal-title":"J. Math. Sociol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102301","DOI":"10.1016\/j.ipm.2020.102301","article-title":"Exploiting Open Data to Analyze Discussion and Controversy in Online Citizen Participation","volume":"57","author":"Cantador","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1108\/S2050-206020160000011021","article-title":"Sentiment Analysis of Polarizing Topics in Social Media: News Site Readers\u2019 Comments on the Trayvon Martin Controversy","volume":"Volume 11","author":"Ignatow","year":"2016","journal-title":"Communication and Information Technologies Annual"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Choi, Y., Jung, Y., and Myaeng, S.-H. (2010). Identifying Controversial Issues and Their Sub-Topics in News Articles, Springer.","DOI":"10.1007\/978-3-642-13601-6_16"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Popescu, A.-M., and Pennacchiotti, M. (2010, January 26\u201330). Detecting Controversial Events from Twitter. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada.","DOI":"10.1145\/1871437.1871751"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Beelen, K., Kanoulas, E., and Van De Velde, B. (2017, January 7\u201311). Detecting Controversies in Online News Media. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","DOI":"10.1145\/3077136.3080723"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102996","DOI":"10.1016\/j.ipm.2022.102996","article-title":"Quantifying the Structural and Temporal Characteristics of Negative Links in Signed Citation Networks","volume":"59","author":"Song","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"26113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and Evaluating Community Structure in Networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cogan, P., Andrews, M., Bradonjic, M., Kennedy, W.S., Sala, A., and Tucci, G. (2012, January 12). Reconstruction and Analysis of Twitter Conversation Graphs. Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, Beijing, China.","DOI":"10.1145\/2392622.2392626"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3140565","article-title":"Quantifying Controversy on Social Media","volume":"1","author":"Garimella","year":"2018","journal-title":"ACM Trans. Soc. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1007\/s10618-017-0527-9","article-title":"Measuring and Moderating Opinion Polarization in Social Networks","volume":"31","author":"Matakos","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.osnem.2017.10.001","article-title":"Automatic Controversy Detection in Social Media: A Content-Independent Motif-Based Approach","volume":"3\u20134","author":"Coletto","year":"2017","journal-title":"Online Soc. Networks Media"},{"key":"ref_25","unstructured":"Mikolov, T., Chen, K., Corrado, G.S., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_26","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s11280-022-01116-0","article-title":"A Text and GNN Based Controversy Detection Method on Social Media","volume":"26","author":"Benslimane","year":"2023","journal-title":"World Wide Web"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bonchi, F., Galimberti, E., Gionis, A., Ordozgoiti, B., and Ruffo, G. (2019, January 3\u20137). Discovering Polarized Communities in Signed Networks. Proceedings of the 28th Acm International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357977"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"eabq2044","DOI":"10.1126\/sciadv.abq2044","article-title":"Quantifying Ideological Polarization on a Network Using Generalized Euclidean Distance","volume":"9","author":"Hohmann","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1140\/epjds\/s13688-024-00480-3","article-title":"Quantifying Polarization in Online Political Discourse","volume":"13","year":"2024","journal-title":"EPJ Data Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111580","DOI":"10.1016\/j.knosys.2024.111580","article-title":"Heterogeneous Network Influence Maximization Algorithm Based on Multi-Scale Propagation Strength and Repulsive Force of Propagation Field","volume":"291","author":"Guo","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121821","DOI":"10.1016\/j.eswa.2023.121821","article-title":"Integrating Heterogeneous Structures and Community Semantics for Unsupervised Community Detection in Heterogeneous Networks","volume":"238","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1007\/s11280-023-01191-x","article-title":"Quantifying Controversy from Stance, Sentiment, Offensiveness and Sarcasm: A Fine-Grained Controversy Intensity Measurement Framework on a Chinese Dataset","volume":"26","author":"Wang","year":"2023","journal-title":"World Wide Web"},{"key":"ref_34","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3560487","article-title":"User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network","volume":"41","author":"Cai","year":"2023","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). Node2vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.neucom.2019.07.052","article-title":"Improving Text Classification with Weighted Word Embeddings via a Multi-Channel TextCNN Model","volume":"363","author":"Guo","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s13278-020-00703-1","article-title":"A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)","volume":"10","author":"Emamgholizadeh","year":"2020","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"033114","DOI":"10.1063\/1.4913758","article-title":"Measuring Political Polarization: Twitter Shows the Two Sides of Venezuela","volume":"25","author":"Morales","year":"2015","journal-title":"Chaos"},{"key":"ref_41","unstructured":"Conover, M., Ratkiewicz, J., Francisco, M., Gon\u00e7alves, B., Menczer, F., and Flammini, A. (2011, January 17\u201321). Political Polarization on Twitter. Proceedings of the International Aaai Conference on Web and Social Media, Barcelona, Spain."},{"key":"ref_42","first-page":"423","article-title":"Automated Controversy Detection on the Web","volume":"Volume 37","author":"Allan","year":"2015","journal-title":"Proceedings of the Advances in Information Retrieval: 37th European Conference on IR Research"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"121942","DOI":"10.1016\/j.techfore.2022.121942","article-title":"Polarization and Social Media: A Systematic Review and Research Agenda","volume":"183","author":"Arora","year":"2022","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Matsumoto, N., Moran, J., Choi, H., Hernandez, M.E., Venkatesan, M., Wang, P., and Moore, J.H. (2024). KRAGEN: A knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics, 40.","DOI":"10.1093\/bioinformatics\/btae353"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/473\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:22:36Z","timestamp":1760030556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,27]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["e27050473"],"URL":"https:\/\/doi.org\/10.3390\/e27050473","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2025,4,27]]}}}