{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T09:40:37Z","timestamp":1735551637398,"version":"3.32.0"},"reference-count":87,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["437085\/2018-0"],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro - FAPERJ","award":["E26\/211.194\/2019"]},{"name":"Rede Nacional de Ensino e Pesquisa (RNP)","award":["GT-RLProvideMi"]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["2018\/23062-5"],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"City hall of Niter\u00f3i\/FEC\/UFF","award":["PDPA 2020"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.<\/jats:p>","DOI":"10.3390\/info12010038","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T10:17:34Z","timestamp":1610965054000},"page":"38","source":"Crossref","is-referenced-by-count":67,"title":["Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2034-6899","authenticated-orcid":false,"given":"Nicollas R.","family":"de Oliveira","sequence":"first","affiliation":[{"name":"LabGen\/M\u00eddiaCom\u2013PPGEET\/TCE\/IC\/UFF, Universidade Federal Fluminense (UFF), Niter\u00f3i 24210-240, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4238-0876","authenticated-orcid":false,"given":"Pedro S.","family":"Pisa","sequence":"additional","affiliation":[{"name":"Solvimm, Rio de Janeiro 20090-902, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4170-4341","authenticated-orcid":false,"given":"Martin Andreoni","family":"Lopez","sequence":"additional","affiliation":[{"name":"Technology Innovation Institute (TII), Abu Dhabi 9639, UAE"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-5903","authenticated-orcid":false,"given":"Dianne Scherly V.","family":"de Medeiros","sequence":"additional","affiliation":[{"name":"LabGen\/M\u00eddiaCom\u2013PPGEET\/TCE\/IC\/UFF, Universidade Federal Fluminense (UFF), Niter\u00f3i 24210-240, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-7366","authenticated-orcid":false,"given":"Diogo M. F.","family":"Mattos","sequence":"additional","affiliation":[{"name":"LabGen\/M\u00eddiaCom\u2013PPGEET\/TCE\/IC\/UFF, Universidade Federal Fluminense (UFF), Niter\u00f3i 24210-240, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/LSP.2020.3008087","article-title":"A Sensitive Stylistic Approach to Identify Fake News on Social Networking","volume":"27","author":"Medeiros","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_2","unstructured":"Liu, G., Wang, Y., and Orgun, M.A. (2010, January 9\u201313). Quality of trust for social trust path selection in complex social networks. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto, ON, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, X., and Zafarani, R. (2020). A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Comput. Surv., 53.","DOI":"10.1145\/3395046"},{"key":"ref_5","unstructured":"Wang, W.Y. (August, January 30). \u201cLiar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_6","first-page":"32","article-title":"On deception and deception detection: Content analysis of computer-mediated stated beliefs","volume":"Volume 47","author":"Rubin","year":"2010","journal-title":"Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rubin, V., Conroy, N., Chen, Y., and Cornwell, S. (2016). Fake news or truth? using satirical cues to detect potentially misleading news. Proceedings of the Second Workshop on Computational Approaches to Deception Detection, Association for Computational Linguistics.","DOI":"10.18653\/v1\/W16-0802"},{"key":"ref_8","unstructured":"Rubin, V.L., Chen, Y., and Conroy, N.J. (2015, January 6\u201310). Deception detection for news: Three types of fakes. Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, Silver Spring, MD, USA."},{"key":"ref_9","unstructured":"Rubin, V.L., Conroy, N.J., and Chen, Y. (2015, January 5\u20138). Towards news verification: Deception detection methods for news discourse. Proceedings of the Hawaii International Conference on System Sciences, Kauai, HI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014, January 23\u201325). The Stanford CoreNLP natural language processing toolkit. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-5010"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3305260","article-title":"Combating fake news: A survey on identification and mitigation techniques","volume":"10","author":"Sharma","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Golbeck, J., Mauriello, M., Auxier, B., Bhanushali, K.H., Bonk, C., Bouzaghrane, M.A., Buntain, C., Chanduka, R., Cheakalos, P., and Everett, J.B. (2018). Fake News vs Satire: A Dataset and Analysis, Association for Computing Machinery. WebSci \u201918.","DOI":"10.1145\/3201064.3201100"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Y., Conroy, N.J., and Rubin, V.L. (2015, January 13). Misleading online content: Recognizing clickbait as false news. Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection, New York, NY, USA.","DOI":"10.1145\/2823465.2823467"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.dss.2008.11.001","article-title":"Decision support for determining veracity via linguistic-based cues","volume":"46","author":"Fuller","year":"2009","journal-title":"Decis. Support Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sharma, S., and Sharma, D.K. (2019, January 21\u201322). Fake News Detection: A long way to go. Proceedings of the 2019 4th International Conference on Information Systems and Computer Networks (ISCON), Mathura, UP, India.","DOI":"10.1109\/ISCON47742.2019.9036221"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Davis, C.A., Varol, O., Ferrara, E., Flammini, A., and Menczer, F. (2016). BotOrNot: A System to Evaluate Social Bots. Proceedings of the 25th International Conference Companion on World Wide Web, WWW \u201916 Companion, International World Wide Web Conferences Steering Committee.","DOI":"10.1145\/2872518.2889302"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13174-019-0106-y","article-title":"An agile and effective network function virtualization infrastructure for the Internet of Things","volume":"10","author":"Mattos","year":"2019","journal-title":"J. Internet Serv. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., and Wang, Y. (2013, January 7\u201310). Prominent features of rumor propagation in online social media. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, New York, NY, USA.","DOI":"10.1109\/ICDM.2013.61"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, X., Cao, J., Jin, Z., Xie, F., Su, Y., Chu, D., Cao, X., and Zhang, J. (2015, January 18\u201322). Real-time news certification system on Sina Weibo. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.","DOI":"10.1145\/2740908.2742571"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108384","DOI":"10.1109\/ACCESS.2019.2932018","article-title":"Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Santia, G.C., and Williams, J.R. (2018). Buzzface: A news veracity dataset with facebook user commentary and egos. Twelfth International AAAI Conference on Web and Social Media, AAAI.","DOI":"10.1609\/icwsm.v12i1.14985"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Monteiro, R.A., Santos, R.L., Pardo, T.A., de Almeida, T.A., Ruiz, E.E., and Vale, O.A. (2018). Contributions to the Study of Fake News in Portuguese: New Corpus and Automatic Detection Results. International Conference on Computational Processing of the Portuguese Language, Springer.","DOI":"10.1007\/978-3-319-99722-3_33"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ferreira, W., and Vlachos, A. (2016, January 12\u201317). Emergent: A novel data-set for stance classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1138"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A., Christodoulopoulos, C., and Mittal, A. (2018, January 1\u20136). FEVER: A Large-scale Dataset for Fact Extraction and VERification. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-1074"},{"key":"ref_27","unstructured":"Mitra, T., and Gilbert, E. (2015). Credbank: A large-scale social media corpus with associated credibility annotations. ICWSM, AAAI."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.18653\/v1\/P18-1022","article-title":"A stylometric inquiry into hyperpartisan and fake news","volume":"Volume 1","author":"Potthast","year":"2018","journal-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., and Tolmie, P. (2016). Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150989"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., and Choi, Y. (2017, January 9\u201311). Truth of varying shades: Analyzing language in fake news and political fact-checking. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1317"},{"key":"ref_31","unstructured":"Oshikawa, R., Qian, J., and Wang, W.Y. (2018). A survey on natural language processing for fake news detection. Proceedings of the 12th Language Resources and Evaluation Conference (LREC), European Language Resources Association."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rubin, V.L. (2014). Pragmatic and cultural considerations for deception detection in Asian Languages. ACM Trans. Asian Lang. Inf. Process., 13.","DOI":"10.1145\/2605292"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.ipm.2011.07.002","article-title":"Automatically structuring domain knowledge from text: An overview of current research","volume":"48","author":"Clark","year":"2012","journal-title":"Inf. Process. Manag."},{"key":"ref_34","unstructured":"Otter, D.W., Medina, J.R., and Kalita, J.K. (2020). A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Netw. Learning Syst., 1\u201321."},{"key":"ref_35","unstructured":"Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python, O\u2019Reilly Media, Inc.. [1st ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1023\/B:GRUP.0000011944.62889.6f","article-title":"Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications","volume":"13","author":"Zhou","year":"2004","journal-title":"Group Decis. Negotiat."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Afroz, S., Brennan, M., and Greenstadt, R. (2012). Detecting hoaxes, frauds, and deception in writing style online. 2012 IEEE Symposium on Security and Privacy, IEEE.","DOI":"10.1109\/SP.2012.34"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1177\/1088868314556539","article-title":"Are computers effective lie detectors? A meta-analysis of linguistic cues to deception","volume":"19","author":"Hauch","year":"2015","journal-title":"Personal. Soc. Psychol. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Indurkhya, N., and Damerau, F.J. (2010). Handbook of Natural Language Processing, Chapman & Hall\/CRC. [2nd ed.].","DOI":"10.1201\/9781420085938"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"de Oliveira, N.R., Reis, L.H., Fernandes, N.C., Bastos, C.A.M., de Medeiros, D.S.V., and Mattos, D.M.F. (2020, January 17\u201320). Natural Language Processing Characterization of Recurring Calls in Public Security Services. Proceedings of the 2020 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA.","DOI":"10.1109\/ICNC47757.2020.9049821"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1459352.1459355","article-title":"Word sense disambiguation: A survey","volume":"41","author":"Navigli","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_42","unstructured":"Manning, C., and Schutze, H. (1999). Foundations of Statistical Natural Language Processing, MIT Press."},{"key":"ref_43","unstructured":"Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics."},{"key":"ref_44","unstructured":"Pennebaker, J.W., Francis, M.E., and Booth, R.J. (2001). Inquiry and Word Count: LIWC 2001, Lawrence Erlbaum Associates."},{"key":"ref_45","unstructured":"Balage Filho, P., Pardo, T.A.S., and Alu\u00edsio, S. (2013, January 21\u201323). An evaluation of the Brazilian Portuguese LIWC dictionary for sentiment analysis. Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology (STIL), Fortaleza, Brazil."},{"key":"ref_46","first-page":"100","article-title":"Introduction to information retrieval","volume":"16","author":"Manning","year":"2010","journal-title":"Natural Lang. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jarmasz, M., and Szpakowicz, S. (2003). Not as Easy as It Seems: Automating the Construction of Lexical Chains Using Roget\u2019s Thesaurus. Advances in Artificial Intelligence, Springer.","DOI":"10.1007\/3-540-44886-1_48"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1613\/jair.1.11259","article-title":"From word to sense embeddings: A survey on vector representations of meaning","volume":"63","author":"Pilehvar","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_49","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013, January 2\u20134). Efficient estimation of word representations in vector space. Proceedings of the ICLR Workshop Papers, Scottsdale, AZ, USA."},{"key":"ref_50","first-page":"168","article-title":"A synopsis of linguistic theory, 1930\u20131955","volume":"1","author":"Firth","year":"1957","journal-title":"Studi. Linguist. Anal."},{"key":"ref_51","unstructured":"Li, Y., Xu, L., Tian, F., Jiang, L., Zhong, X., and Chen, E. (2015). Word embedding revisited: A new representation learning and explicit matrix factorization perspective. Twenty-Fourth International Joint Conference on Artificial Intelligence, AAAI Press."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1016\/j.neucom.2015.07.046","article-title":"A novel word embedding learning model using the dissociation between nouns and verbs","volume":"171","author":"Hu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13174-018-0087-2","article-title":"A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities","volume":"9","author":"Boutaba","year":"2018","journal-title":"J. Internet Serv. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A few useful things to know about machine learning","volume":"55","author":"Domingos","year":"2012","journal-title":"ACM Commun."},{"key":"ref_55","unstructured":"Ayodele, T.O. (2010). Types of machine learning algorithms. New Advances in Machine Learning, IntechOpen."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MCI.2014.2326099","article-title":"The emerging \u201cbig dimensionality\u201d","volume":"9","author":"Zhai","year":"2014","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12243-018-0663-2","article-title":"A fast unsupervised preprocessing method for network monitoring","volume":"74","author":"Mattos","year":"2019","journal-title":"Ann. Telecommun."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1006\/jcss.2000.1711","article-title":"Latent semantic indexing: A probabilistic analysis","volume":"61","author":"Papadimitriou","year":"2000","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9","article-title":"Indexing by latent semantic analysis","volume":"41","author":"Deerwester","year":"1990","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and Empirical Analysis of ReliefF and RReliefF","volume":"53","author":"Kononenko","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Perdisci, R., Gu, G., and Lee, W. (2006, January 18\u201322). Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems. Proceedings of the Sixth International Conference on Data Mining (ICDM\u201906), Hong Kong, China.","DOI":"10.1109\/ICDM.2006.165"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Gaonkar, S., Itagi, S., Chalippatt, R., Gaonkar, A., Aswale, S., and Shetgaonkar, P. (2019, January 30\u201331). Detection Of Online Fake News: A Survey. Proceedings of the 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India.","DOI":"10.1109\/ViTECoN.2019.8899556"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.patcog.2010.08.011","article-title":"Mining data with random forests: A survey and results of new tests","volume":"44","author":"Verikas","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10462-018-09677-1","article-title":"Survey on supervised machine learning techniques for automatic text classification","volume":"52","author":"Kadhim","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TETC.2014.2330519","article-title":"A survey of clustering algorithms for big data: Taxonomy and empirical analysis","volume":"2","author":"Fahad","year":"2014","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G","article-title":"The application of cluster analysis in strategic management research: An analysis and critique","volume":"17","author":"Ketchen","year":"1996","journal-title":"Strateg. Manag. J."},{"key":"ref_69","unstructured":"Rousseeuw, P.J., and Kaufman, L. (1990). Finding Groups in Data, Wiley Online Library."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Gan, J., and Tao, Y. (2015). DBSCAN revisited: Mis-claim, un-fixability, and approximation. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery.","DOI":"10.1145\/2723372.2737792"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3068335","article-title":"DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.future.2019.05.029","article-title":"FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images","volume":"100","author":"Benavent","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.apr.2019.09.009","article-title":"Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980\u20132019)","volume":"11","author":"Govender","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1126\/science.aao2998","article-title":"The science of fake news","volume":"359","author":"Lazer","year":"2018","journal-title":"Science"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1126\/science.aau2706","article-title":"Fake news on Twitter during the 2016 US presidential election","volume":"363","author":"Grinberg","year":"2019","journal-title":"Science"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1177\/0956797620939054","article-title":"Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention","volume":"31","author":"Pennycook","year":"2020","journal-title":"Psychol. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Van Bavel, J.J., Baicker, K., Boggio, P.S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M.J., Crum, A.J., Douglas, K.M., and Druckman, J.N. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nat. Hum. Behav., 1\u201312.","DOI":"10.31234\/osf.io\/y38m9"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Poddar, K., Umadevi, K., and Amali, G.B. (2019, January 22\u201323). Comparison of Various Machine Learning Models for Accurate Detection of Fake News. Proceedings of the 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India.","DOI":"10.1109\/i-PACT44901.2019.8960044"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s10588-018-09280-3","article-title":"FakeNewsTracker: A tool for fake news collection, detection, and visualization","volume":"25","author":"Shu","year":"2019","journal-title":"Comput. Math. Organ. Theory"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ins.2019.05.035","article-title":"A survey on fake news and rumour detection techniques","volume":"497","author":"Bondielli","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1016\/j.procs.2020.03.276","article-title":"Deep neural approach to Fake-News identification","volume":"167","author":"Deepak","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018, January 13\u201319). Neural User Response Generator: Fake News Detection with Collective User Intelligence. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, (IJCAI-18), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/533"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Friggeri, A., Adamic, L., Eckles, D., and Cheng, J. (2014, January 1\u20134). Rumor Cascades. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14559"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"112986","DOI":"10.1016\/j.eswa.2019.112986","article-title":"Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities","volume":"153","author":"Meel","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"de Oliveira, N.R., de Medeiros, D.S.V., and Mattos, D.M.F. (2020, January 7\u201312). A Syntactic-Relationship Approach to Construct Well-Informative Knowledge Graphs Representation. Proceedings of the 4th Cloud and Internet of Things (CIoT\u201920), Niter\u00f3i, Brazil.","DOI":"10.1109\/CIoT50422.2020.9244288"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Guan, H., Bhat, M.M., and Hsu, J. (2019, January 19\u201321). Fake news detection via NLP is vulnerable to adversarial attacks. Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART), Prague, Czech Republic.","DOI":"10.5220\/0007566307940800"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.sbspro.2017.02.067","article-title":"Analyzing Informal Learning Patterns in Facebook Communities of International Conferences","volume":"237","author":"Giglio","year":"2017","journal-title":"Procedia Soc. Behav. Sci."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/1\/38\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T09:18:57Z","timestamp":1735550337000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/1\/38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":87,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["info12010038"],"URL":"https:\/\/doi.org\/10.3390\/info12010038","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2021,1,18]]}}}