{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:16:00Z","timestamp":1771960560118,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030914332","type":"print"},{"value":"9783030914349","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91434-9_29","type":"book-chapter","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T07:04:42Z","timestamp":1638515082000},"page":"330-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fake News Detection Using LDA Topic Modelling and K-Nearest Neighbor Classifier"],"prefix":"10.1007","author":[{"given":"Mario","family":"Casillo","sequence":"first","affiliation":[]},{"given":"Francesco","family":"Colace","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4929-4698","authenticated-orcid":false,"given":"Brij B.","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5783-1847","authenticated-orcid":false,"given":"Domenico","family":"Santaniello","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9964-1104","authenticated-orcid":false,"given":"Carmine","family":"Valentino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,4]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"di Renzo, L., et al.: Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey. J. Transl. Med. 18(1) (2020). https:\/\/doi.org\/10.1186\/s12967-020-02399-5","DOI":"10.1186\/s12967-020-02399-5"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Herrera-Peco, I., et al.: Antivaccine movement and COVID-19 negationism: a content analysis of Spanish-written messages on Twitter. Vaccines 9(6) (2021). https:\/\/doi.org\/10.3390\/vaccines9060656","DOI":"10.3390\/vaccines9060656"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"York, C., Ponder, J.D., Humphries, Z., Goodall, C., Beam, M., Winters, C.: Effects of fact-checking political misinformation on perceptual accuracy and epistemic political efficacy. J. Mass Commun. Q. 97(4) (2020). https:\/\/doi.org\/10.1177\/1077699019890119","DOI":"10.1177\/1077699019890119"},{"key":"29_CR4","doi-asserted-by":"publisher","unstructured":"Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media. ACM SIGKDD Explor. Newsl. 19(1) (2017). https:\/\/doi.org\/10.1145\/3137597.3137600","DOI":"10.1145\/3137597.3137600"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2) (2017). https:\/\/doi.org\/10.1257\/jep.31.2.211","DOI":"10.1257\/jep.31.2.211"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53(5) (2020). https:\/\/doi.org\/10.1145\/3395046","DOI":"10.1145\/3395046"},{"key":"29_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-981-15-1275-9_13","volume-title":"Advances in Computational Intelligence and Communication Technology","author":"SR Sahoo","year":"2021","unstructured":"Sahoo, S.R., Gupta, B.B.: Real-time detection of fake account in Twitter using machine-learning approach. In: Gao, X.-Z., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds.) Advances in Computational Intelligence and Communication Technology. AISC, vol. 1086, pp. 149\u2013159. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-1275-9_13"},{"key":"29_CR8","doi-asserted-by":"publisher","unstructured":"Przyby\u0142a, P.: Capturing the style of fake news (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5386","DOI":"10.1609\/aaai.v34i01.5386"},{"key":"29_CR9","doi-asserted-by":"publisher","unstructured":"Nagaraja, A., Soumya, K.N., Naik, P., Sinha, A., Rajendrakumar, J.V.: Fake news detection using machine learning methods (2021). https:\/\/doi.org\/10.1145\/3460620.3460753","DOI":"10.1145\/3460620.3460753"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Ozbay, F.A., Alatas, B.: Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Stat. Mech. Appl. 540 (2020). https:\/\/doi.org\/10.1016\/j.physa.2019.123174","DOI":"10.1016\/j.physa.2019.123174"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Shu, K., Liu, H.: Detecting fake news on social media. Synthesis Lectures Data Min. Knowl. Discov. 11(3) (2019). https:\/\/doi.org\/10.2200\/s00926ed1v01y201906dmk018","DOI":"10.2200\/s00926ed1v01y201906dmk018"},{"key":"29_CR12","doi-asserted-by":"publisher","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4\u20135) (2003). https:\/\/doi.org\/10.1016\/b978-0-12-411519-4.00006-9","DOI":"10.1016\/b978-0-12-411519-4.00006-9"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Clarizia, F., Colace, F., Lombardi, M., Pascale, F., Santaniello, D.: Sentiment analysis in social networks: a methodology based on the latent Dirichlet allocation approach, August 2019. https:\/\/doi.org\/10.2991\/eusflat-19.2019.36","DOI":"10.2991\/eusflat-19.2019.36"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Colace, F., Casaburi, L., de Santo, M., Greco, L.: Sentiment detection in social networks and in collaborative learning environments. Comput. Hum. Behav. 51 (2015). https:\/\/doi.org\/10.1016\/j.chb.2014.11.090","DOI":"10.1016\/j.chb.2014.11.090"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Hu, Q., Yu, D., Xie, Z.: Neighborhood classifiers. Expert Syst. Appl. 34(2) (2008). https:\/\/doi.org\/10.1016\/j.eswa.2006.10.043","DOI":"10.1016\/j.eswa.2006.10.043"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Dong, W., Charikar, M., Li, K.: Efficient K-nearest neighbor graph construction for generic similarity measures (2011). https:\/\/doi.org\/10.1145\/1963405.1963487","DOI":"10.1145\/1963405.1963487"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Jiang, L., Cai, Z., Wang, D., Jiang, S.: Survey of improving K-nearest-neighbor for classification. In: Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 1 (2007). https:\/\/doi.org\/10.1109\/FSKD.2007.552","DOI":"10.1109\/FSKD.2007.552"},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10 (2009). https:\/\/doi.org\/10.1145\/1577069.1577078","DOI":"10.1145\/1577069.1577078"},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Kesarwani, A., Chauhan, S.S., Nair, A.R.: Fake news detection on social media using k-nearest neighbor classifier (2020). https:\/\/doi.org\/10.1109\/ICACCE49060.2020.9154997","DOI":"10.1109\/ICACCE49060.2020.9154997"},{"key":"29_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/978-3-030-68787-8_22","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"M Casillo","year":"2021","unstructured":"Casillo, M., Conte, D., Lombardi, M., Santaniello, D., Valentino, C.: Recommender system for digital storytelling: a novel approach to enhance cultural heritage. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12667, pp. 304\u2013317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68787-8_22"},{"key":"29_CR21","doi-asserted-by":"publisher","unstructured":"Colace, F., Lombardi, M., Pascale, F., Santaniello, D.: A multi-level approach for forecasting critical events in smart cities (2018). https:\/\/doi.org\/10.18293\/DMSVIVA2018-002","DOI":"10.18293\/DMSVIVA2018-002"},{"key":"29_CR22","doi-asserted-by":"publisher","unstructured":"Clarizia, F., Colace, F., de Santo, M., Lombardi, M., Pascale, F., Santaniello, D.: A context-aware chatbot for tourist destinations. In: 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 348\u2013354, November 2019. https:\/\/doi.org\/10.1109\/SITIS.2019.00063","DOI":"10.1109\/SITIS.2019.00063"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Fari\u00f1a, A., Brisaboa, N.R., Navarro, G., Claude, F., Places, \u00c1.S., Rodr\u00edguez, E.: Word-based self-indexes for natural language text. ACM Trans. Inf. Syst. 30(1) (2012). https:\/\/doi.org\/10.1145\/2094072.2094073","DOI":"10.1145\/2094072.2094073"},{"key":"29_CR24","unstructured":"Wilson, A.T., Chew, P.A.: Term weighting schemes for latent Dirichlet allocation (2010)"},{"key":"29_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-030-66046-8_27","volume-title":"Computational Data and Social Networks","author":"M Casillo","year":"2020","unstructured":"Casillo, M., et al.: A multi-feature Bayesian approach for fake news detection. In: Chellappan, S., Choo, K.-K., Phan, NhatHai (eds.) CSoNet 2020. LNCS, vol. 12575, pp. 333\u2013344. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66046-8_27"},{"key":"29_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/978-3-030-50423-6_49","volume-title":"Computational Science \u2013 ICCS 2020","author":"S Kula","year":"2020","unstructured":"Kula, S., Chora\u015b, M., Kozik, R., Ksieniewicz, P., Wo\u017aniak, M.: Sentiment analysis for fake news detection by means of neural networks. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12140, pp. 653\u2013666. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50423-6_49"},{"key":"29_CR27","doi-asserted-by":"publisher","unstructured":"Bhutani, B., Rastogi, N., Sehgal, P., Purwar, A.: Fake news detection using sentiment analysis (2019). https:\/\/doi.org\/10.1109\/IC3.2019.8844880","DOI":"10.1109\/IC3.2019.8844880"},{"key":"29_CR28","unstructured":"Mitra, T., Gilbert, E.: CREDBANK: a large-scale social media corpus with associated credibility annotations (2015)"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91434-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:44:04Z","timestamp":1710233044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91434-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030914332","9783030914349"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91434-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"4 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Data and Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/csonet-conf.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}