{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T19:22:07Z","timestamp":1774898527176,"version":"3.50.1"},"reference-count":33,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.<\/jats:p>","DOI":"10.2298\/csis230315048t","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T11:31:06Z","timestamp":1696937466000},"page":"1439-1457","source":"Crossref","is-referenced-by-count":2,"title":["A framework for fake news detection based on the wisdom of crowds and the ensemble learning model"],"prefix":"10.2298","volume":"20","author":[{"suffix":"Bang","given":"Hai","family":"Truong","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, University of Technology National University, Ho Chi Minh City, Vietnam"}]},{"suffix":"Cuong","given":"Van","family":"Tran","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, Quang Binh University Quang Binh, Vietnam"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Abbasi, M.A., Liu, H.: Measuring user credibility in social media. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. pp. 441-448. Springer (2013)","DOI":"10.1007\/978-3-642-37210-0_48"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: 2012 IEEE Symposium on Security and Privacy. pp. 461-475. IEEE (2012)","DOI":"10.1109\/SP.2012.34"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Awal, G.K., Bharadwaj, K.K.: Team formation in social networks based on collective intelligence-an evolutionary approach. Applied intelligence 41, 627-648 (2014)","DOI":"10.1007\/s10489-014-0528-y"},{"key":"ref4","unstructured":"B\u00f6hringer, M., Helmholz, P.: \u201cwhat are they thinking?\u201d-accessing collective intelligence in twitter (2011)"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Bondielli, A., Marcelloni, F.: A survey on fake news and rumour detection techniques. Information Sciences 497, 38-55 (2019)","DOI":"10.1016\/j.ins.2019.05.035"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Della Vedova, M.L., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., de Alfaro, L.: Automatic online fake news detection combining content and social signals. In: 2018 22nd Conference of Open Innovations Association (FRUCT). pp. 272-279. IEEE (2018)","DOI":"10.23919\/FRUCT.2018.8468301"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Duong, T.H., Nguyen, N.T., Jo, G.S.: A hybrid method for integrating multiple ontologies. Cybernetics and Systems: An International Journal 40(2), 123-145 (2009)","DOI":"10.1080\/01969720802634055"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Gholami, B., Safavi, R.: Harnessing collective intelligence:Wiki and social network from enduser perspective. In: 2010 International Conference on e-Education, e-Business, e-Management and e-Learning. pp. 242-246. IEEE (2010)","DOI":"10.1109\/IC4E.2010.49"},{"key":"ref9","unstructured":"Groza, A.: Detecting fake news for the new coronavirus by reasoning on the covid-19 ontology. arXiv preprint arXiv:2004.12330 (2020)"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Hakak, S., Alazab, M., Khan, S., Gadekallu, T.R., Maddikunta, P.K.R., Khan, W.Z.: An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems 117, 47-58 (2021)","DOI":"10.1016\/j.future.2020.11.022"},{"key":"ref11","unstructured":"Hangloo, S., Arora, B.: Fake news detection tools and methods-a review. arXiv preprint arXiv:2112.11185 (2021)"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Hardalov, M., Koychev, I., Nakov, P.: In search of credible news. In: International Conference on Artificial Intelligence: Methodology, Systems, and Applications. pp. 172-180. Springer (2016)","DOI":"10.1007\/978-3-319-44748-3_17"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Zhang, Y., Luo, J.: News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the AAAI conference on artificial intelligence. vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10382"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Karnyoto, A.S., Sun, C., Liu, B., Wang, X.: Transfer learning and gru-crf augmentation for covid-19 fake news detection. Computer Science and Information Systems (00), 53-53 (2022)","DOI":"10.2298\/CSIS210501053K"},{"key":"ref15","unstructured":"Katarzyniak, R., Nguyen, N.T.: Reconciling inconsistent profiles of agents\u2019 knowledge states in distributed multiagent systems using consensus methods. Systems Science 26(4), 93-119 (2000)"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining. pp. 1103- 1108. IEEE (2013)","DOI":"10.1109\/ICDM.2013.61"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T.: Conflicts of ontologies-classification and consensus-based methods for resolving. In: Knowledge-Based Intelligent Information and Engineering Systems: 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006. Proceedings, Part II 10. pp. 267-274. Springer (2006)","DOI":"10.1007\/11893004_34"},{"key":"ref18","unstructured":"P\u00b4erez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)"},{"key":"ref19","unstructured":"Qin, Y., Wurzer, D., Lavrenko, V., Tang, C.: Spotting rumors via novelty detection. arXiv preprint arXiv:1611.06322 (2016)"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: Analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 2931-2937 (2017)","DOI":"10.18653\/v1\/D17-1317"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Sahoo, S.R., Gupta, B.B.: Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing 100, 106983 (2021)","DOI":"10.1016\/j.asoc.2020.106983"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Seddari, N., Derhab, A., Belaoued, M., Halboob, W., Al-Muhtadi, J., Bouras, A.: A hybrid linguistic and knowledge-based analysis approach for fake news detection on social media. IEEE Access 10, 62097-62109 (2022)","DOI":"10.1109\/ACCESS.2022.3181184"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1-9 (2018)","DOI":"10.1038\/s41467-018-06930-7"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Shu, K., Bernard, H.R., Liu, H.: Studying fake news via network analysis: detection and mitigation. In: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining, pp. 43-65 (2019)","DOI":"10.1007\/978-3-319-94105-9_3"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19(1), 22-36 (2017)","DOI":"10.1145\/3137597.3137600"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Shu, K.,Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). pp. 430-435. IEEE (2018)","DOI":"10.1109\/MIPR.2018.00092"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Shu, K., Wang, S., Liu, H.: Beyond news contents: The role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. pp. 312-320. ACM (2019)","DOI":"10.1145\/3289600.3290994"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Sliwko, L., Nguyen, N.T.: Using multi-agent systems and consensus methods for information retrieval in internet. International Journal of Intelligent Information and Database Systems 1(2), 181-198 (2007)","DOI":"10.1504\/IJIIDS.2007.014949"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Tran, V.C., Nguyen, V.D., Nguyen, N.T.: Automatic fake news detection by exploiting user\u2019s assessments on social networks: A case study of twitter. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. pp. 373-384. Springer (2020)","DOI":"10.1007\/978-3-030-55789-8_33"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhang, Z., Zhang, M., Chen, W., Zeng, D.D.: Combining crowd and machine intelligence to detect false news on social media. Mis Quarterly (2019)","DOI":"10.2139\/ssrn.3355763"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Yang, S., Shu, K., Wang, S., Gu, R., Wu, F., Liu, H.: Unsupervised fake news detection on social media: A generative approach. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 5644-5651 (2019)","DOI":"10.1609\/aaai.v33i01.33015644"},{"key":"ref32","unstructured":"Zhou, X., Zafarani, R.: Fake news: A survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315 2 (2018)"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR) 53(5), 1-40 (2020","DOI":"10.1145\/3395046"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T09:26:46Z","timestamp":1722245206000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142300048T"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis230315048t","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}