{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T09:58:08Z","timestamp":1763978288678,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031157769"},{"type":"electronic","value":"9783031157776"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-15777-6_26","type":"book-chapter","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T15:24:33Z","timestamp":1661268273000},"page":"472-491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FN2: Fake News DetectioN Based on\u00a0Textual and\u00a0Contextual Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1577-1161","authenticated-orcid":false,"given":"Mouna","family":"Rabhi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8964-0746","authenticated-orcid":false,"given":"Spiridon","family":"Bakiras","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1909-0336","authenticated-orcid":false,"given":"Roberto","family":"Di Pietro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"26_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-319-69155-8_9","volume-title":"Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments","author":"H Ahmed","year":"2017","unstructured":"Ahmed, H., Traore, I., Saad, S.: Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127\u2013138. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69155-8_9"},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"152183","DOI":"10.1109\/ACCESS.2020.3017382","volume":"8","author":"L Cai","year":"2020","unstructured":"Cai, L., Song, Y., Liu, T., Zhang, K.: A hybrid BERT model that incorporates label semantics via adjustive attention for multi-label text classification. IEEE Access 8, 152183\u2013152192 (2020)","journal-title":"IEEE Access"},{"key":"26_CR3","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-981-15-3380-8_49","volume-title":"Intelligent Information and Database Systems","author":"B Collins","year":"2020","unstructured":"Collins, B., Hoang, D.T., Nguyen, N.T., Hwang, D.: Fake News Types and Detection Models on Social Media A State-of-the-Art Survey. In: Sitek, P., Pietranik, M., Kr\u00f3tkiewicz, M., Srinilta, C. (eds.) ACIIDS 2020. CCIS, vol. 1178, pp. 562\u2013573. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-3380-8_49"},{"key":"26_CR4","doi-asserted-by":"publisher","first-page":"130042","DOI":"10.1109\/ACCESS.2021.3113877","volume":"9","author":"TH Do","year":"2021","unstructured":"Do, T.H., Berneman, M., Patro, J., Bekoulis, G., Deligiannis, N.: Context-aware deep Markov random fields for fake news detection. IEEE Access 9, 130042\u2013130054 (2021)","journal-title":"IEEE Access"},{"key":"26_CR5","unstructured":"Elisa Shearer, K.E.M.: News use across social media platforms 2018, August 2020"},{"key":"26_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115414","volume":"183","author":"PMS Freire","year":"2021","unstructured":"Freire, P.M.S., da Silva, F.R.M., Goldschmidt, R.R.: Fake news detection based on explicit and implicit signals of a hybrid crowd: an approach inspired in meta-learning. Expert Syst. Appl. 183, 115414 (2021)","journal-title":"Expert Syst. Appl."},{"key":"26_CR7","unstructured":"Google-Research: Google-research\/bert: Tensorflow code and pre-trained models for bert (2019). https:\/\/github.com\/google-research\/bert"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Granik, M., Mesyura, V.: Fake news detection using Naive Bayes classifier. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900\u2013903. IEEE (2017)","DOI":"10.1109\/UKRCON.2017.8100379"},{"key":"26_CR9","doi-asserted-by":"publisher","unstructured":"Junker, M., Hoch, R., Dengel, A.: On the evaluation of document analysis components by recall, precision, and accuracy. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR 1999 (Cat. No.PR00318), pp. 713\u2013716 (1999). https:\/\/doi.org\/10.1109\/ICDAR.1999.791887","DOI":"10.1109\/ICDAR.1999.791887"},{"issue":"14","key":"26_CR10","doi-asserted-by":"publisher","first-page":"8597","DOI":"10.1007\/s00521-020-05611-1","volume":"33","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar, R.K., Goswami, A., Narang, P.: EchoFakeD: improving fake news detection in social media with an efficient deep neural network. Neural Comput. Appl. 33(14), 8597\u20138613 (2021). https:\/\/doi.org\/10.1007\/s00521-020-05611-1","journal-title":"Neural Comput. Appl."},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.cogsys.2019.12.005","volume":"61","author":"RK Kaliyar","year":"2020","unstructured":"Kaliyar, R.K., Goswami, A., Narang, P., Sinha, S.: FNDNet-a deep convolutional neural network for fake news detection. Cogn. Syst. Res. 61, 32\u201344 (2020)","journal-title":"Cogn. Syst. Res."},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Kaliyar, R.K., Kumar, P., Kumar, M., Narkhede, M., Namboodiri, S., Mishra, S.: DeepNet: an efficient neural network for fake news detection using news-user engagements. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICCCS49678.2020.9277353"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Martino, G.D.S., Cresci, S., Barr\u00f3n-Cede\u00f1o, A., Yu, S., Di Pietro, R., Nakov, P.: A survey on computational propaganda detection. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 4826\u20134832 (2020). ijcai.org","DOI":"10.24963\/ijcai.2020\/672"},{"issue":"1","key":"26_CR14","volume":"1","author":"JA Nasir","year":"2021","unstructured":"Nasir, J.A., Khan, O.S., Varlamis, I.: Fake news detection: a hybrid CNN-RNN based deep learning approach. Int. J. Inf. Manag. Data Insights 1(1), 100007 (2021)","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"26_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/978-3-030-00671-6_39","volume-title":"The Semantic Web \u2013 ISWC 2018","author":"JZ Pan","year":"2018","unstructured":"Pan, J.Z., Pavlova, S., Li, C., Li, N., Li, Y., Liu, J.: Content Based Fake News Detection Using Knowledge Graphs. In: Vrande\u010di\u0107, D., Bontcheva, K., Su\u00e1rez-Figueroa, M.C., Presutti, V., Celino, I., Sabou, M., Kaffee, L.-A., Simperl, E. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 669\u2013683. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00671-6_39"},{"key":"26_CR16","unstructured":"Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates 71(2001), 2001 (2001)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., Stein, B.: A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638 (2017)","DOI":"10.18653\/v1\/P18-1022"},{"key":"26_CR18","doi-asserted-by":"publisher","DOI":"10.1145\/3522756","author":"S Raponi","year":"2022","unstructured":"Raponi, S., Khalifa, Z., Oligeri, G., Di Pietro, R.: Fake news propagation: a review of epidemic models, datasets, and insights. ACM Trans. Web (2022). https:\/\/doi.org\/10.1145\/3522756","journal-title":"ACM Trans. Web"},{"key":"26_CR19","unstructured":"Rapoza, K.: Can \u2018fake news\u2019 impact the stock market? December 2020. https:\/\/www.forbes.com\/sites\/kenrapoza\/2017\/02\/26\/can-fake-news-impact-the-stock-market\/?sh=f625ee52fac0"},{"key":"26_CR20","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\u20132937 (2017)","DOI":"10.18653\/v1\/D17-1317"},{"issue":"3","key":"26_CR21","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1089\/big.2020.0062","volume":"8","author":"K Shu","year":"2020","unstructured":"Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: Fakenewsnet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171\u2013188 (2020)","journal-title":"Big Data"},{"key":"26_CR22","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\u2013320 (2019)","DOI":"10.1145\/3289600.3290994"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Shu, K., Zhou, X., Wang, S., Zafarani, R., Liu, H.: The role of user profiles for fake news detection. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 436\u2013439 (2019)","DOI":"10.1145\/3341161.3342927"},{"key":"26_CR24","unstructured":"Tankovska, H.: Number of social network users worldwide from 2017 to 2025, January 2021. https:\/\/www.statista.com\/statistics\/278414\/number-of-worldwide-social-network-users\/"},{"key":"26_CR25","doi-asserted-by":"publisher","first-page":"156695","DOI":"10.1109\/ACCESS.2020.3019735","volume":"8","author":"M Umer","year":"2020","unstructured":"Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G.S., On, B.W.: Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access 8, 156695\u2013156706 (2020)","journal-title":"IEEE Access"},{"key":"26_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 5998\u20136008 (2017)"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Verma, A., Mittal, V., Dawn, S.: FIND: fake information and news detections using deep learning. In: 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/IC3.2019.8844892"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Wang, W.Y.: Liar, liar pants on fire: a new benchmark dataset for fake news detection. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 422\u2013426 (2017)","DOI":"10.18653\/v1\/P17-2067"},{"key":"26_CR29","unstructured":"Yang, Y., Zheng, L., Zhang, J., Cui, Q., Li, Z., Yu, P.S.: TI-CNN: convolutional neural networks for fake news detection. arXiv preprint arXiv:1806.00749 (2018)"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Dong, B., Yu, P.S.: Deep diffusive neural network based fake news detection from heterogeneous social networks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1259\u20131266. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005556"},{"key":"26_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-030-47436-2_27","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Wu, J., Zafarani, R.: $$\\sf SAFE$$: similarity-aware multi-modal fake news detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 354\u2013367. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_27"},{"issue":"5","key":"26_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395046","volume":"53","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. (CSUR) 53(5), 1\u201340 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"}],"container-title":["Lecture Notes in Computer Science","Information and Communications Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15777-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:37:54Z","timestamp":1709829474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15777-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031157769","9783031157776"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15777-6_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}