{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:37:36Z","timestamp":1743010656387,"version":"3.40.3"},"publisher-location":"Cham","reference-count":91,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031105449"},{"type":"electronic","value":"9783031105456"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10545-6_5","type":"book-chapter","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T06:03:02Z","timestamp":1658469782000},"page":"57-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Misinformation Detection in\u00a0Social Networks: A Systematic Literature Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-8309","authenticated-orcid":false,"given":"Zafer","family":"Duzen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4991-3455","authenticated-orcid":false,"given":"Mirela","family":"Riveni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-5067","authenticated-orcid":false,"given":"Mehmet S.","family":"Aktas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"27840","DOI":"10.1109\/ACCESS.2021.3058066","volume":"9","author":"DS Abdelminaam","year":"2021","unstructured":"Abdelminaam, D.S., Ismail, F.H., Taha, M., Taha, A., Houssein, E.H., Nabil, A.: CoAID-DEEP: an optimized intelligent framework for automated detecting COVID-19 misleading information on Twitter. IEEE Access 9, 27840\u201327867 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3058066","journal-title":"IEEE Access"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Acula, D.D., Oblan, L.A.C., Pedroso, T.B., Riosa, K.J.V., Tolibas, M.A.R.: Implementing fact-checking in journalistic articles shared on social media in the Philippines using knowledge graphs. In: 2018 3rd International Conference on Computer and Communication Systems (ICCCS), pp. 462\u2013466. IEEE (2018)","DOI":"10.1109\/CCOMS.2018.8463282"},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"155961","DOI":"10.1109\/ACCESS.2020.3019600","volume":"8","author":"MS Al-Rakhami","year":"2020","unstructured":"Al-Rakhami, M.S., Al-Amri, A.M.: Lies kill, facts save: detecting COVID-19 misinformation in Twitter. IEEE Access 8, 155961\u2013155970 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019600","journal-title":"IEEE Access"},{"key":"5_CR4","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1049\/ise2.12021","volume":"15","author":"M Albahar","year":"2021","unstructured":"Albahar, M.: A hybrid model for fake news detection: leveraging news content and user comments in fake news. IET Inf. Secur. 15, 169\u2013177 (2021). https:\/\/doi.org\/10.1049\/ise2.12021","journal-title":"IET Inf. Secur."},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Baeth, M.J., Aktas, M.: On the detection of information pollution and violation of copyrights in the social web. In: 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications (SOCA), pp. 252\u2013254 (2015). https:\/\/doi.org\/10.1109\/SOCA.2015.27","DOI":"10.1109\/SOCA.2015.27"},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Baeth, M.J., Aktas, M.S.: Detecting misinformation in social networks using provenance data. In: 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), pp. 85\u201389 (2017). https:\/\/doi.org\/10.1109\/SKG.2017.00022","DOI":"10.1109\/SKG.2017.00022"},{"issue":"3","key":"5_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4793","volume":"31","author":"MJ Baeth","year":"2019","unstructured":"Baeth, M.J., Aktas, M.S.: Detecting misinformation in social networks using provenance data. Concurr. Comput. Pract. Exp. 31(3), e4793 (2019)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"5_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3039168","author":"M Bahja","year":"2020","unstructured":"Bahja, M., Safdar, G.A.: Unlink the link between COVID-19 and 5G networks: an NLP and SNA based approach. IEEE Access (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3039168","journal-title":"IEEE Access"},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00767-7","volume":"11","author":"T Balasubramaniam","year":"2021","unstructured":"Balasubramaniam, T., Nayak, R., Luong, K., Bashar, M.A.: Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization. Soc. Netw. Anal. Min. 11(1), 1\u201319 (2021). https:\/\/doi.org\/10.1007\/s13278-021-00767-7","journal-title":"Soc. Netw. Anal. Min."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Benamira, A., Devillers, B., Lesot, E., Ray, A.K., Saadi, M., Malliaros, F.D.: Semi-supervised learning and graph neural networks for fake news detection. In: 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 568\u2013569. IEEE (2019)","DOI":"10.1145\/3341161.3342958"},{"issue":"1","key":"5_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s13735-017-0143-x","volume":"7","author":"C Boididou","year":"2017","unstructured":"Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L., Papadopoulou, O., Kompatsiaris, Y.: Detection and visualization of misleading content on Twitter. Int. J. Multimedia Inf. Retrieval 7(1), 71\u201386 (2017). https:\/\/doi.org\/10.1007\/s13735-017-0143-x","journal-title":"Int. J. Multimedia Inf. Retrieval"},{"issue":"5","key":"5_CR12","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1007\/s11063-020-10365-x","volume":"53","author":"AMP Bra\u015foveanu","year":"2020","unstructured":"Bra\u015foveanu, A.M.P., Andonie, R.: Integrating machine learning techniques in semantic fake news detection. Neural Process. Lett. 53(5), 3055\u20133072 (2020). https:\/\/doi.org\/10.1007\/s11063-020-10365-x","journal-title":"Neural Process. Lett."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Cao, H., Deng, J., Dong, G., Yuan, D.: A discriminative graph neural network for fake news detection. In: 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 224\u2013228. IEEE (2021)","DOI":"10.1109\/ICBASE53849.2021.00049"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Chatfield, A.T., Reddick, C.G., Choi, K.: Online media use of false news to frame the 2016 trump presidential campaign. In: Proceedings of the 18th Annual International Conference on Digital Government Research, pp. 213\u2013222 (2017)","DOI":"10.1145\/3085228.3085295"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Exploiting behavioral differences to detect fake news. In: 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 879\u2013884. IEEE (2018)","DOI":"10.1109\/UEMCON.2018.8796519"},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"5449","DOI":"10.1002\/int.22518","volume":"36","author":"X Chen","year":"2021","unstructured":"Chen, X., Zhou, F., Zhang, F., Bonsangue, M.: Modeling microscopic and macroscopic information diffusion for rumor detection. Int. J. Intell. Syst. 36, 5449\u20135471 (2021). https:\/\/doi.org\/10.1002\/int.22518","journal-title":"Int. J. Intell. Syst."},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Cheng, L., Guo, R., Shu, K., Liu, H.: Causal understanding of fake news dissemination on social media. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 148\u2013157 (2021)","DOI":"10.1145\/3447548.3467321"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Chughtai, M.A., Hou, J., Long, H., Li, Q., Ismail, M.: Design of a predictor for Covid-19 misinformation prediction. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1\u20137. IEEE (2021)","DOI":"10.1109\/ICIC53490.2021.9693057"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Conti, M., Lain, D., Lazzeretti, R., Lovisotto, G., Quattrociocchi, W.: It\u2019s always april fools\u2019 day!: on the difficulty of social network misinformation classification via propagation features. In: 2017 IEEE Workshop on Information Forensics and Security (WIFS), pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/WIFS.2017.8267653","DOI":"10.1109\/WIFS.2017.8267653"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Dhiman, A., Toshniwal, D.: An unsupervised misinformation detection framework to analyze the users using COVID-19 Twitter data. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 679\u2013688. IEEE (2020)","DOI":"10.1109\/BigData50022.2020.9378250"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Ganesh, P., Priya, L., Nandakumar, R.: Fake news detection-a comparative study of advanced ensemble approaches. In: 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1003\u20131008. IEEE (2021)","DOI":"10.1109\/ICOEI51242.2021.9453061"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Garg, R., Jeevaraj, S.: Effective fake news classifier and its applications to COVID-19. In: 2021 IEEE Bombay Section Signature Conference (IBSSC), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/IBSSC53889.2021.9673448"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Gautam, A., Jerripothula, K.R.: SGG: Spinbot, Grammarly and GloVe based fake news detection. In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pp. 174\u2013182. IEEE (2020)","DOI":"10.1109\/BigMM50055.2020.00033"},{"key":"5_CR24","doi-asserted-by":"publisher","DOI":"10.1177\/0165551520985486","author":"A Giachanou","year":"2021","unstructured":"Giachanou, A., Ghanem, B., Rosso, P.: Detection of conspiracy propagators using psycho-linguistic characteristics. J. Inf. Sci. (2021). https:\/\/doi.org\/10.1177\/0165551520985486","journal-title":"J. Inf. Sci."},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Gupta, A., Kaushal, R.: Improving spam detection in online social networks. In: 2015 International Conference on Cognitive Computing and Information Processing (CCIP), pp. 1\u20136 (2015). https:\/\/doi.org\/10.1109\/CCIP.2015.7100738","DOI":"10.1109\/CCIP.2015.7100738"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Hande, A., Puranik, K., Priyadharshini, R., Thavareesan, S., Chakravarthi, B.R.: Evaluating pretrained transformer-based models for COVID-19 fake news detection. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 766\u2013772. IEEE (2021)","DOI":"10.1109\/ICCMC51019.2021.9418446"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Hassan, F.M., Lee, M.: Political fake statement detection via multistage feature-assisted neural modeling. In: 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ISI49825.2020.9280531"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Heidari, M., et al.: BERT model for fake news detection based on social bot activities in the COVID-19 pandemic. In: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0103\u20130109. IEEE (2021)","DOI":"10.1109\/UEMCON53757.2021.9666618"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Hinderks, A., Jos\u00e9, F., Mayo, D., Thomaschewski, J., Escalona, M.J.: An SLR-tool: search process in practice: a tool to conduct and manage systematic literature review (SLR). In: 2020 IEEE\/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pp. 81\u201384 (2020)","DOI":"10.1145\/3377812.3382137"},{"key":"5_CR30","unstructured":"Huang, B., Carley, K.M.: Disinformation and misinformation on twitter during the novel coronavirus outbreak. arXiv preprint arXiv:2006.04278 (2020)"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Hussna, A.U., Trisha, I.I., Karim, M.S., Alam, M.G.R.: COVID-19 fake news prediction on social media data. In: 2021 IEEE Region 10 Symposium (TENSYMP), pp. 1\u20135. IEEE (2021)","DOI":"10.1109\/TENSYMP52854.2021.9550957"},{"key":"5_CR32","doi-asserted-by":"publisher","unstructured":"Jain, D.K., Kumar, A., Shrivastava, A.: CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams. Neural Comput. Appl. 1\u201312 (2021). https:\/\/doi.org\/10.1007\/s00521-021-06743-8","DOI":"10.1007\/s00521-021-06743-8"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Janakieva, D., Mirceva, G., Gievska, S.: Fake news detection by using Doc2Vec representation model and various classification algorithms. In: 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), pp. 223\u2013228. IEEE (2021)","DOI":"10.23919\/MIPRO52101.2021.9596928"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Jing, Q., et al.: TRANSFAKE: multi-task transformer for multimodal enhanced fake news detection. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533433"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Kaliyar, R.K.: Fake news detection using a deep neural network. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/CCAA.2018.8777343"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Kaliyar, R.K., Goswami, A., Narang, P.: MCNNet: generalizing fake news detection with a multichannel convolutional neural network using a novel COVID-19 dataset. In: 8th ACM IKDD CODS and 26th COMAD, pp. 437\u2013437. Association for Computing Machinery (2021)","DOI":"10.1145\/3430984.3431064"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Kaliyar, R.K., Singh, N.: Misinformation detection on online social media-a survey. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944587","DOI":"10.1109\/ICCCNT45670.2019.8944587"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Kar, D., Bhardwaj, M., Samanta, S., Azad, A.P.: No rumours please! A multi-indic-lingual approach for COVID fake-tweet detection. In: 2021 Grace Hopper Celebration India (GHCI), pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/GHCI50508.2021.9514012"},{"key":"5_CR39","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1080\/07421222.2021.1990611","volume":"38","author":"KK King","year":"2021","unstructured":"King, K.K., Wang, B., Escobari, D., Oraby, T.: Dynamic effects of falsehoods and corrections on social media: a theoretical modeling and empirical evidence. J. Manag. Inf. Syst. 38, 989\u20131010 (2021). https:\/\/doi.org\/10.1080\/07421222.2021.1990611","journal-title":"J. Manag. Inf. Syst."},{"issue":"2004","key":"5_CR40","first-page":"1","volume":"33","author":"B Kitchenham","year":"2004","unstructured":"Kitchenham, B.: Procedures for performing systematic reviews. Keele UK Keele Univ. 33(2004), 1\u201326 (2004)","journal-title":"Keele UK Keele Univ."},{"key":"5_CR41","doi-asserted-by":"publisher","DOI":"10.1201\/b19467","volume-title":"Evidence-Based Software Engineering and Systematic Reviews","author":"BA Kitchenham","year":"2015","unstructured":"Kitchenham, B.A., Budgen, D., Brereton, P.: Evidence-Based Software Engineering and Systematic Reviews, vol. 4. CRC Press, Boca Raton (2015)"},{"key":"5_CR42","doi-asserted-by":"publisher","unstructured":"Kumar, A., Bhatia, M.P.S., Sangwan, S.R.: Rumour detection using deep learning and filter-wrapper feature selection in benchmark Twitter dataset. Multimedia Tools Appl. 1\u201318 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11340-x","DOI":"10.1007\/s11042-021-11340-x"},{"issue":"1","key":"5_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13673-014-0014-x","volume":"4","author":"KPK Kumar","year":"2014","unstructured":"Kumar, K.P.K., Geethakumari, G.: Detecting misinformation in online social networks using cognitive psychology. HCIS 4(1), 1\u201322 (2014). https:\/\/doi.org\/10.1186\/s13673-014-0014-x","journal-title":"HCIS"},{"key":"5_CR44","doi-asserted-by":"publisher","unstructured":"Kumar, A., Bhatia, M.P.S., Sangwan, S.R.: Rumour detection using deep learning and filter-wrapper feature selection in benchmark Twitter dataset. Multimedia Tools Appl. 1\u201318 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11340-x","DOI":"10.1007\/s11042-021-11340-x"},{"key":"5_CR45","doi-asserted-by":"publisher","unstructured":"Luo, Y., Ma, J., Yeo, C.K.: Exploiting user network topology and comment semantic for accurate rumour stance recognition on social media. J. Inf. Sci. (2020). https:\/\/doi.org\/10.1177\/0165551520977443","DOI":"10.1177\/0165551520977443"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Malhotra, B., Vishwakarma, D.K.: Classification of propagation path and tweets for rumor detection using graphical convolutional networks and transformer based encodings. In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pp. 183\u2013190. IEEE, Institute of Electrical and Electronics Engineers Inc. (2020)","DOI":"10.1109\/BigMM50055.2020.00034"},{"issue":"1","key":"5_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjds\/s13688-021-00301-x","volume":"10","author":"M Mattei","year":"2021","unstructured":"Mattei, M., Caldarelli, G., Squartini, T., Saracco, F.: Italian Twitter semantic network during the Covid-19 epidemic. EPJ Data Sci. 10(1), 1\u201327 (2021). https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00301-x","journal-title":"EPJ Data Sci."},{"issue":"1","key":"5_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00738-y","volume":"11","author":"D Mehta","year":"2021","unstructured":"Mehta, D., Dwivedi, A., Patra, A., Anand Kumar, M.: A transformer-based architecture for fake news classification. Soc. Netw. Anal. Min. 11(1), 1\u201312 (2021). https:\/\/doi.org\/10.1007\/s13278-021-00738-y","journal-title":"Soc. Netw. Anal. Min."},{"key":"5_CR49","unstructured":"Memon, S.A., Carley, K.M.: Characterizing COVID-19 misinformation communities using a novel twitter dataset. arXiv preprint arXiv:2008.00791 (2020)"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Menczer, F.: The spread of misinformation in social media. In: Proceedings of the 25th International Conference Companion on World Wide Web (2016)","DOI":"10.1145\/2872518.2890092"},{"key":"5_CR51","doi-asserted-by":"publisher","unstructured":"Mohapatra, A., Thota, N., Prakasam, P.: Fake news detection and classification using hybrid BiLSTM and self-attention model. Multimedia Tools Appl. (2022). https:\/\/doi.org\/10.1007\/s11042-022-12764-9. https:\/\/link.springer.com\/10.1007\/s11042-022-12764-9","DOI":"10.1007\/s11042-022-12764-9"},{"key":"5_CR52","doi-asserted-by":"crossref","unstructured":"Muri\u0107, G., Wu, Y., Ferrara, E.: COVID-19 vaccine hesitancy on social media: building a public Twitter data set of antivaccine content, vaccine misinformation, and conspiracies. JMIR Public Health Surveillance 7, e30642 (2021)","DOI":"10.2196\/30642"},{"key":"5_CR53","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1080\/07421222.2021.1990612","volume":"38","author":"KC Ng","year":"2021","unstructured":"Ng, K.C., Tang, J., Lee, D.: The effect of platform intervention policies on fake news dissemination and survival: an empirical examination. J. Manag. Inf. Syst. 38, 898\u2013930 (2021). https:\/\/doi.org\/10.1080\/07421222.2021.1990612","journal-title":"J. Manag. Inf. Syst."},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Nguyen, N.P., Thai, M.T.: Sources of misinformation in online social networks: who to suspect? In: MILCOM 2012\u20132012 IEEE Military Communications Conference, pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/MILCOM.2012.6415780"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"Nguyen, V.H., Sugiyama, K., Nakov, P., Kan, M.Y.: Fang: Leveraging social context for fake news detection using graph representation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1165\u20131174 (2020)","DOI":"10.1145\/3340531.3412046"},{"issue":"6","key":"5_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00836-w","volume":"2","author":"DVB de Oliveira","year":"2021","unstructured":"de Oliveira, D.V.B., Albuquerque, U.P.: Cultural evolution and digital media: diffusion of fake news about COVID-19 on Twitter. SN Comput. Sci. 2(6), 1\u201312 (2021). https:\/\/doi.org\/10.1007\/s42979-021-00836-w","journal-title":"SN Comput. Sci."},{"key":"5_CR57","doi-asserted-by":"publisher","unstructured":"Palani, B., Elango, S., Viswanathan K.V.: CB-Fake: a multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT. Multimedia Tools Appl. 1\u201334 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11782-3","DOI":"10.1007\/s11042-021-11782-3"},{"key":"5_CR58","doi-asserted-by":"publisher","unstructured":"Peng, X., Xintong, B.: An effective strategy for multi-modal fake news detection. Multimedia Tools Appl. 81, 13799\u201313822 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12290-8. https:\/\/link.springer.com\/10.1007\/s11042-022-12290-8","DOI":"10.1007\/s11042-022-12290-8"},{"key":"5_CR59","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1177\/0956797620939054","volume":"31","author":"G Pennycook","year":"2020","unstructured":"Pennycook, G., McPhetres, J., Zhang, Y., Lu, J.G., Rand, D.G.: Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy nudge intervention. Psychol. Sci. 31, 770\u2013780 (2020)","journal-title":"Psychol. Sci."},{"issue":"1","key":"5_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjds\/s13688-020-00253-8","volume":"9","author":"F Pierri","year":"2020","unstructured":"Pierri, F., Piccardi, C., Ceri, S.: A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter. EPJ Data Sci. 9(1), 1\u201317 (2020). https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00253-8","journal-title":"EPJ Data Sci."},{"key":"5_CR61","doi-asserted-by":"crossref","unstructured":"Raju, R., Bhandari, S., Mohamud, S.A., Ceesay, E.N.: Transfer learning model for disrupting misinformation during a COVID-19 pandemic. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0245\u20130250. IEEE (2021)","DOI":"10.1109\/CCWC51732.2021.9376066"},{"key":"5_CR62","doi-asserted-by":"crossref","unstructured":"Rani, N., Das, P., Bhardwaj, A.K.: A hybrid deep learning model based on CNN-BiLSTM for rumor detection. In: 2021 6th International Conference on Communication and Electronics Systems (ICCES), pp. 1423\u20131427. IEEE (2021)","DOI":"10.1109\/ICCES51350.2021.9489214"},{"issue":"1","key":"5_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-018-0540-z","volume":"8","author":"B Rath","year":"2018","unstructured":"Rath, B., Gao, W., Ma, J., Srivastava, J.: Utilizing computational trust to identify rumor spreaders on Twitter. Soc. Netw. Anal. Min. 8(1), 1\u201316 (2018). https:\/\/doi.org\/10.1007\/s13278-018-0540-z","journal-title":"Soc. Netw. Anal. Min."},{"key":"5_CR64","doi-asserted-by":"crossref","unstructured":"Rath, B., Gao, W., Srivastava, J.: Evaluating vulnerability to fake news in social networks: a community health assessment model. In: 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 432\u2013435. IEEE (2019)","DOI":"10.1145\/3341161.3342920"},{"key":"5_CR65","doi-asserted-by":"publisher","unstructured":"Raza, S., Ding, C.: Fake news detection based on news content and social contexts: a transformer-based approach. Int. J. Data Sci. Anal. 1\u201328 (2021). https:\/\/doi.org\/10.1007\/s41060-021-00302-z","DOI":"10.1007\/s41060-021-00302-z"},{"key":"5_CR66","doi-asserted-by":"publisher","unstructured":"R\u00f6chert, D., Shahi, G.K., Neubaum, G., Ross, B., Stieglitz, S.: The networked context of COVID-19 misinformation: informational homogeneity on Youtube at the beginning of the pandemic. Online Soc. Netw. Media 26, 100164 (2021). https:\/\/doi.org\/10.1016\/j.osnem.2021.100164","DOI":"10.1016\/j.osnem.2021.100164"},{"key":"5_CR67","doi-asserted-by":"publisher","first-page":"129471","DOI":"10.1109\/ACCESS.2021.3112806","volume":"9","author":"H Saleh","year":"2021","unstructured":"Saleh, H., Alharbi, A., Alsamhi, S.H.: OPCNN-FAKE: optimized convolutional neural network for fake news detection. IEEE Access 9, 129471\u2013129489 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3112806","journal-title":"IEEE Access"},{"issue":"1","key":"5_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-020-00634-x","volume":"10","author":"S Santhoshkumar","year":"2020","unstructured":"Santhoshkumar, S., Dhinesh Babu, L.D.: Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks. Soc. Netw. Anal. Min. 10(1), 1\u201317 (2020). https:\/\/doi.org\/10.1007\/s13278-020-00634-x","journal-title":"Soc. Netw. Anal. Min."},{"key":"5_CR69","doi-asserted-by":"publisher","unstructured":"Shelke, S., Attar, V.: Rumor detection in social network based on user, content and lexical features. Multimedia Tools Appl. 81, 17347\u201317368 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12761-y. https:\/\/link.springer.com\/10.1007\/s11042-022-12761-y","DOI":"10.1007\/s11042-022-12761-y"},{"key":"5_CR70","doi-asserted-by":"publisher","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, 171\u2013188 (2020). https:\/\/doi.org\/10.1089\/big.2020.0062","DOI":"10.1089\/big.2020.0062"},{"key":"5_CR71","doi-asserted-by":"crossref","unstructured":"Sridhar, S., Sanagavarapu, S.: Fake news detection and analysis using multitask learning with BiLSTM CapsNet model. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 905\u2013911. IEEE, Institute of Electrical and Electronics Engineers Inc. (2021)","DOI":"10.1109\/Confluence51648.2021.9377080"},{"key":"5_CR72","doi-asserted-by":"crossref","unstructured":"Surendran, P., Navyasree, B., Kambham, H., Kumar, M.A.: Covid-19 fake news detector using hybrid convolutional and Bi-LSTM model. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 01\u201306. IEEE, Institute of Electrical and Electronics Engineers (IEEE) (2021)","DOI":"10.1109\/ICCCNT51525.2021.9579994"},{"key":"5_CR73","unstructured":"Tambuscio, M., Oliveira, D.F.M., Ciampaglia, G.L., Ruffo, G.: Network segregation in a model of misinformation and fact checking. CoRR abs\/1610.04170 (2016). http:\/\/arxiv.org\/abs\/1610.04170"},{"key":"5_CR74","doi-asserted-by":"crossref","unstructured":"Tarnpradab, S., Hua, K.A.: Attention based neural architecture for rumor detection with author context awareness. In: 2018 Thirteenth International Conference on Digital Information Management (ICDIM), pp. 82\u201387. IEEE (2018)","DOI":"10.1109\/ICDIM.2018.8847052"},{"key":"5_CR75","doi-asserted-by":"crossref","unstructured":"Thakur, A., Shinde, S., Patil, T., Gaud, B., Babanne, V.: MYTHYA: fake news detector, real time news extractor and classifier. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), pp. 982\u2013987. IEEE (2020)","DOI":"10.1109\/ICOEI48184.2020.9142971"},{"key":"5_CR76","doi-asserted-by":"crossref","unstructured":"Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., Krause, A.: Fake news detection in social networks via crowd signals. In: Companion Proceedings of the Web Conference 2018, pp. 517\u2013524 (2018)","DOI":"10.1145\/3184558.3188722"},{"key":"5_CR77","doi-asserted-by":"publisher","unstructured":"Tyagi, S., Pai, A., Pegado, J., Kamath, A.: A proposed model for preventing the spread of misinformation on online social media using machine learning. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 678\u2013683 (2019). https:\/\/doi.org\/10.1109\/AICAI.2019.8701408","DOI":"10.1109\/AICAI.2019.8701408"},{"key":"5_CR78","doi-asserted-by":"crossref","unstructured":"Vogel, I., Meghana, M.: Detecting fake news spreaders on Twitter from a multilingual perspective. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 599\u2013606. IEEE (2020)","DOI":"10.1109\/DSAA49011.2020.00084"},{"key":"5_CR79","doi-asserted-by":"crossref","unstructured":"Volkova, S., Jang, J.Y.: Misleading or falsification: inferring deceptive strategies and types in online news and social media. In: Companion Proceedings of the Web Conference 2018, pp. 575\u2013583 (2018)","DOI":"10.1145\/3184558.3188728"},{"key":"5_CR80","doi-asserted-by":"publisher","unstructured":"Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146\u20131151 (2018). https:\/\/doi.org\/10.1126\/science.aap9559. https:\/\/www.science.org\/doi\/abs\/10.1126\/science.aap9559","DOI":"10.1126\/science.aap9559"},{"key":"5_CR81","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1177\/1075547017731776","volume":"39","author":"EK Vraga","year":"2017","unstructured":"Vraga, E.K., Bode, L.: Using expert sources to correct health misinformation in social media. Sci. Commun. 39, 621\u2013645 (2017)","journal-title":"Sci. Commun."},{"issue":"1","key":"5_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-019-0580-z","volume":"9","author":"L Wang","year":"2019","unstructured":"Wang, L., Wang, Y., de Melo, G., Weikum, G.: Understanding archetypes of fake news via fine-grained classification. Soc. Netw. Anal. Min. 9(1), 1\u201317 (2019). https:\/\/doi.org\/10.1007\/s13278-019-0580-z","journal-title":"Soc. Netw. Anal. Min."},{"key":"5_CR83","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 849\u2013857. Association for Computing Machinery (2018)","DOI":"10.1145\/3219819.3219903"},{"key":"5_CR84","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, F., Wang, H., Jha, K., Gao, J.: Multimodal emergent fake news detection via meta neural process networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3708\u20133716. Association for Computing Machinery (2021)","DOI":"10.1145\/3447548.3467153"},{"key":"5_CR85","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qian, S., Hu, J., Fang, Q., Xu, C.: Fake news detection via knowledge-driven multimodal graph convolutional networks. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 540\u2013547 (2020)","DOI":"10.1145\/3372278.3390713"},{"key":"5_CR86","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1109\/JBHI.2020.3037027","volume":"25","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Yin, Z., Argyris, Y.A.: Detecting medical misinformation on social media using multimodal deep learning. IEEE J. Biomed. Health Inform. 25, 2193\u20132203 (2021). https:\/\/doi.org\/10.1109\/JBHI.2020.3037027","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"5_CR87","doi-asserted-by":"crossref","unstructured":"Watine, P., Bodaghi, A., Schmitt, K.A.: Can the Hawkes process be used to evaluate the spread of online information? In: 2021 IEEE International Symposium on Technology and Society (ISTAS), pp. 1\u20136. IEEE, Institute of Electrical and Electronics Engineers Inc. (2021)","DOI":"10.1109\/ISTAS52410.2021.9629133"},{"key":"5_CR88","doi-asserted-by":"crossref","unstructured":"Xie, Y., Huang, X., Xie, X., Jiang, S.: A fake news detection framework using social user graph. In: Proceedings of the 2020 2nd International Conference on Big Data Engineering, pp. 55\u201361. Association for Computing Machinery (2020)","DOI":"10.1145\/3404512.3404515"},{"key":"5_CR89","doi-asserted-by":"crossref","unstructured":"Yang, Y.: COVID-19 fake news detection via graph neural networks in social media. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 3178\u20133180. IEEE, Institute of Electrical and Electronics Engineers (IEEE) (2021)","DOI":"10.1109\/BIBM52615.2021.9669662"},{"key":"5_CR90","doi-asserted-by":"crossref","unstructured":"Zaeem, R.N., Li, C., Barber, K.S.: On sentiment of online fake news. In: 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 760\u2013767. IEEE (2020)","DOI":"10.1109\/ASONAM49781.2020.9381323"},{"key":"5_CR91","doi-asserted-by":"crossref","unstructured":"Zubiaga, A., Liakata, M., Procter, R., Hoi, G.W.S., Tolmie, P.: Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One 11(3), e0150989 (2016)","DOI":"10.1371\/journal.pone.0150989"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10545-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T17:07:34Z","timestamp":1727629654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10545-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031105449","9783031105456"],"references-count":91,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10545-6_5","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":"23 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","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":"CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"279","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":"57","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":"24","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":"20% - 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":"2.6","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":"8.7","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)"}},{"value":"285 Workshop submission accepted out of 815 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}