{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:26:06Z","timestamp":1781587566780,"version":"3.54.5"},"reference-count":174,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"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":["Int J Multimed Info Retr"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s13735-023-00296-3","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T16:02:28Z","timestamp":1692806548000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunities"],"prefix":"10.1007","volume":"12","author":[{"given":"Shivani","family":"Tufchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashima","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanveer","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"296_CR1","doi-asserted-by":"publisher","unstructured":"Agarwal A, Dixit A (2020) Fake news detection: an ensemble learning approach. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp 1178\u20131183. https:\/\/doi.org\/10.1109\/ICICCS48265.2020.9121030","DOI":"10.1109\/ICICCS48265.2020.9121030"},{"key":"296_CR2","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.procs.2020.01.035","volume":"165","author":"V Agarwal","year":"2019","unstructured":"Agarwal V, Sultana HP, Malhotra S, Sarkar A (2019) Analysis of classifiers for fake news detection. Procedia Comput Sci 165:377\u2013383","journal-title":"Procedia Comput Sci"},{"issue":"42","key":"296_CR3","first-page":"55","volume":"11","author":"K Ahmadi","year":"2021","unstructured":"Ahmadi K, Khafaie T, Ganjoo M (2021) Rumor propagation detection in complex networks based on ILSR model and nodes degree. J Commun Eng 11(42):55\u201368","journal-title":"J Commun Eng"},{"key":"296_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed H, Traore I, Saad S (2017) Detection of online fake news using n-gram analysis and machine learning techniques. In: International conference on intelligent, secure, and dependable systems in distributed and cloud environments, Springer, pp 127\u2013138","DOI":"10.1007\/978-3-319-69155-8_9"},{"key":"296_CR5","unstructured":"Ahmed S, Hinkelmann K, Corradini F (2022) Combining machine learning with knowledge engineering to detect fake news in social networks-a survey. arXiv preprint arXiv:2201.08032"},{"key":"296_CR6","doi-asserted-by":"crossref","unstructured":"Ahmed SR, Sonu\u00e7 E, Ahmed MR, Duru AD (2022) Analysis survey on deepfake detection and recognition with convolutional neural networks. In: 2022 International Congress on Human\u2013Computer Interaction, Optimization and Robotic Applications (HORA), pp 1\u20137","DOI":"10.1109\/HORA55278.2022.9799858"},{"key":"296_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-030-90087-8_2","volume-title":"Combating fake news with computational intelligence techniques","author":"MA Al-Asadi","year":"2022","unstructured":"Al-Asadi MA, Tasdemir S (2022) Using artificial intelligence against the phenomenon of fake news: a systematic literature review. In: Lahby M, Pathan ASK, Maleh Y, Yafooz WMS (eds) Combating fake news with computational intelligence techniques. Springer, Cham, pp 39\u201354"},{"issue":"12","key":"296_CR8","doi-asserted-by":"publisher","first-page":"576","DOI":"10.3390\/info13120576","volume":"13","author":"J Alghamdi","year":"2022","unstructured":"Alghamdi J, Lin Y, Luo S (2022) A comparative study of machine learning and deep learning techniques for fake news detection. Information 13(12):576","journal-title":"Information"},{"issue":"2","key":"296_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","volume":"31","author":"H Allcott","year":"2017","unstructured":"Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211\u201336","journal-title":"J Econ Perspect"},{"key":"296_CR10","unstructured":"Althabiti S, Alsalka MA, Atwell E (2022) SCUoL at CheckThat! 2022: fake news detection using transformer-based models. Working Notes of CLEF"},{"key":"296_CR11","doi-asserted-by":"crossref","unstructured":"Alzaidi MS, Subbalakshmi C, Roshini T, Shukla PK, Shukla SK, Dutta P, Alhassan M (2022) 5G-telecommunication allocation network using IoT enabled improved machine learning technique. Wirel Commun Mobile Comput 2022","DOI":"10.1155\/2022\/6229356"},{"key":"296_CR12","doi-asserted-by":"crossref","unstructured":"Bagade A, Pale A, Sheth S, Agarwal M, Chakrabarti S, Chebrolu K, Sudarshan S (2020) The Kauwa-Kaate fake news detection system. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp 302\u2013306","DOI":"10.1145\/3371158.3371402"},{"key":"296_CR13","unstructured":"Bang YO, Woo SS (2021) Da-FDFtNet: dual attention fake detection fine-tuning network to detect various AI-generated fake images. arXiv preprint arXiv:2112.12001"},{"key":"296_CR14","doi-asserted-by":"crossref","unstructured":"Botnevik B, Sakariassen E, Setty V (2020) Brenda: browser extension for fake news detection. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 2117\u20132120","DOI":"10.1145\/3397271.3401396"},{"issue":"5","key":"296_CR15","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1007\/s11063-020-10365-x","volume":"53","author":"AM Bra\u015foveanu","year":"2021","unstructured":"Bra\u015foveanu AM, Andonie R (2021) Integrating machine learning techniques in semantic fake news detection. Neural Process Lett 53(5):3055\u20133072","journal-title":"Neural Process Lett"},{"key":"296_CR16","doi-asserted-by":"crossref","unstructured":"Brookes S, Waller L (2022) Communities of practice in the production and resourcing of fact-checking. Journalism p 14648849221078465","DOI":"10.1177\/14648849221078465"},{"key":"296_CR17","doi-asserted-by":"crossref","unstructured":"Buntain C, Golbeck J (2017) Automatically identifying fake news in popular twitter threads. In: 2017 IEEE international conference on smart cloud (SmartCloud), pp 208\u2013215","DOI":"10.1109\/SmartCloud.2017.40"},{"issue":"2","key":"296_CR18","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s11587-020-00506-8","volume":"69","author":"B Buonomo","year":"2020","unstructured":"Buonomo B (2020) Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model. Ricerche Mat 69(2):483\u2013499","journal-title":"Ricerche Mat"},{"key":"296_CR19","doi-asserted-by":"crossref","unstructured":"Campan A, Cuzzocrea A, Truta TM (2017) Fighting fake news spread in online social networks: actual trends and future research directions. In: 2017 IEEE International Conference on Big Data (Big Data), pp 4453\u20134457","DOI":"10.1109\/BigData.2017.8258484"},{"key":"296_CR20","doi-asserted-by":"crossref","unstructured":"Chaffey BD (2022) smartinsights. https:\/\/www.smartinsights.com\/social-media-marketing\/social-media-strategy\/new-global-social-media-research\/","DOI":"10.4324\/9781003009498-6"},{"key":"296_CR21","unstructured":"Chatterjee M, Pal S (2019) Busting fake news: need for digital media literacy. In: Rise of the Digital Human, 4th All India Media Conference, Udaipur, Rajasthan. Accessed February, vol 14, p 2022"},{"key":"296_CR22","doi-asserted-by":"crossref","unstructured":"Chaudhary L, Sharma S, Sajwan M (2022) Comparative analysis of supervised machine learning algorithm. Available at SSRN 4143890","DOI":"10.2139\/ssrn.4143890"},{"key":"296_CR23","doi-asserted-by":"crossref","unstructured":"Chen MY, Lai YW, Lian JW (2022a) Using deep learning models to detect fake news about COVID-19. ACM Trans Internet Technol","DOI":"10.1145\/3533431"},{"key":"296_CR24","doi-asserted-by":"crossref","unstructured":"Chen Y, Li D, Zhang P, Sui J, Lv Q, Tun L, Shang L (2022) Cross-modal ambiguity learning for multimodal fake news detection. In: Proceedings of the ACM Web Conference 2022, pp 2897\u20132905","DOI":"10.1145\/3485447.3511968"},{"key":"296_CR25","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.patrec.2022.01.007","volume":"154","author":"H Choi","year":"2022","unstructured":"Choi H, Ko Y (2022) Effective fake news video detection using domain knowledge and multimodal data fusion on youtube. Pattern Recogn Lett 154:44\u201352","journal-title":"Pattern Recogn Lett"},{"key":"296_CR26","doi-asserted-by":"crossref","unstructured":"Choudhary A, Arora A (2021) Imagefake: an ensemble convolution models driven approach for image based fake news detection. In: 2021 7th International Conference on Signal Processing and Communication (ICSC), pp 182\u2013187","DOI":"10.1109\/ICSC53193.2021.9673192"},{"key":"296_CR27","doi-asserted-by":"crossref","unstructured":"Das M, Singh P, Majumdar A (2022) Investigating dynamics of polarization of youtube true and fake news channels. In: Causes and Symptoms of Socio-Cultural Polarization, pp 73\u2013112","DOI":"10.1007\/978-981-16-5268-4_4"},{"key":"296_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116635","volume":"198","author":"M Davoudi","year":"2022","unstructured":"Davoudi M, Moosavi MR, Sadreddini MH (2022) DSS: a hybrid deep model for fake news detection using propagation tree and stance network. Expert Syst Appl 198:116635","journal-title":"Expert Syst Appl"},{"key":"296_CR29","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"296_CR30","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.comcom.2022.01.003","volume":"185","author":"A Dhawan","year":"2022","unstructured":"Dhawan A, Bhalla M, Arora D, Kaushal R, Kumaraguru P (2022) FakeNewsIndia: a benchmark dataset of fake news incidents in India, collection methodology and impact assessment in social media. Comput Commun 185:130\u2013141","journal-title":"Comput Commun"},{"key":"296_CR31","doi-asserted-by":"crossref","unstructured":"Draws T, La\u00a0Barbera D, Soprano M, Roitero K, Ceolin D, Checco A, Mizzaro S (2022) The effects of crowd worker biases in fact-checking tasks. In: 2022 ACM Conference on Fairness, Accountability, and Transparency, pp 2114\u20132124","DOI":"10.1145\/3531146.3534629"},{"key":"296_CR32","unstructured":"Felber T (2021) Constraint 2021: machine learning models for COVID-19 fake news detection shared task. arXiv preprint arXiv:2101.03717"},{"key":"296_CR33","doi-asserted-by":"crossref","unstructured":"Garg S, Sharma DK (2020) New Politifact: a dataset for counterfeit news. In: 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), IEEE, pp 17\u201322","DOI":"10.1109\/SMART50582.2020.9337152"},{"key":"296_CR34","doi-asserted-by":"crossref","unstructured":"Giachanou A, Zhang G, Rosso P (2020) Multimodal multi-image fake news detection. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp 647\u2013654","DOI":"10.1109\/DSAA49011.2020.00091"},{"key":"296_CR35","doi-asserted-by":"crossref","unstructured":"Gilda S (2017) Notice of violation of IEEE publication principles: evaluating machine learning algorithms for fake news detection. In: 2017 IEEE 15th student conference on research and development (SCOReD), pp 110\u2013115","DOI":"10.1109\/SCORED.2017.8305411"},{"key":"296_CR36","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst, 27"},{"issue":"2","key":"296_CR37","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1177\/1461444819856912","volume":"22","author":"J Gray","year":"2020","unstructured":"Gray J, Bounegru L, Venturini T (2020) \u2018Fake news\u2019 as infrastructural uncanny. New Media Soc 22(2):317\u2013341","journal-title":"New Media Soc"},{"key":"296_CR38","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1162\/tacl_a_00454","volume":"10","author":"Z Guo","year":"2022","unstructured":"Guo Z, Schlichtkrull M, Vlachos A (2022) A survey on automated fact-checking. Trans Assoc Comput Linguist 10:178\u2013206","journal-title":"Trans Assoc Comput Linguist"},{"key":"296_CR39","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.future.2020.11.022","volume":"117","author":"S Hakak","year":"2021","unstructured":"Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ (2021) An ensemble machine learning approach through effective feature extraction to classify fake news. Future Gener Comput Syst 117:47\u201358","journal-title":"Future Gener Comput Syst"},{"key":"296_CR40","doi-asserted-by":"crossref","unstructured":"Han Y, Karunasekera S, Leckie C (2020) Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316","DOI":"10.1007\/978-3-030-86340-1_30"},{"key":"296_CR41","doi-asserted-by":"publisher","first-page":"2250036","DOI":"10.1142\/S0219649222500368","volume":"21","author":"S Hannah Nithya","year":"2022","unstructured":"Hannah Nithya S, Sahayadhas A (2022) Automated fake news detection by LSTM enabled with optimal feature selection. J Inf Knowl Manag 21:2250036","journal-title":"J Inf Knowl Manag"},{"issue":"4","key":"296_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501401","volume":"21","author":"F Harrag","year":"2022","unstructured":"Harrag F, Djahli MK (2022) Arabic fake news detection: a fact checking based deep learning approach. Trans Asian Low-Resour Lang Inf Process 21(4):1\u201334","journal-title":"Trans Asian Low-Resour Lang Inf Process"},{"key":"296_CR43","doi-asserted-by":"crossref","unstructured":"He Y, Yu N, Keuper M, Fritz M (2021) Beyond the spectrum: detecting deepfakes via re-synthesis. arXiv preprint arXiv:2105.14376","DOI":"10.24963\/ijcai.2021\/349"},{"issue":"1","key":"296_CR44","first-page":"1","volume":"11","author":"BD Horne","year":"2019","unstructured":"Horne BD, N\u00f8rregaard J, Adali S (2019) Robust fake news detection over time and attack. ACM Trans Intell Syst Technol (TIST) 11(1):1\u201323","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"296_CR45","doi-asserted-by":"crossref","unstructured":"Hsu CC, Lee CY, Zhuang YX (2018) Learning to detect fake face images in the wild. In: 2018 international symposium on computer, consumer and control (IS3C), pp 388\u20133 91","DOI":"10.1109\/IS3C.2018.00104"},{"issue":"1","key":"296_CR46","doi-asserted-by":"publisher","first-page":"370","DOI":"10.3390\/app10010370","volume":"10","author":"CC Hsu","year":"2020","unstructured":"Hsu CC, Zhuang YX, Lee CY (2020) Deep fake image detection based on pairwise learning. Appl Sci 10(1):370","journal-title":"Appl Sci"},{"key":"296_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113584","volume":"159","author":"YF Huang","year":"2020","unstructured":"Huang YF, Chen PH (2020) Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms. Expert Syst Appl 159:113584","journal-title":"Expert Syst Appl"},{"key":"296_CR48","doi-asserted-by":"crossref","unstructured":"Ibrishimova MD, Li KF (2019) A machine learning approach to fake news detection using knowledge verification and natural language processing. In: International Conference on Intelligent Networking and Collaborative Systems, pp 223\u2013234","DOI":"10.1007\/978-3-030-29035-1_22"},{"issue":"1","key":"296_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-020-00696-x","volume":"10","author":"MR Islam","year":"2020","unstructured":"Islam MR, Liu S, Wang X, Xu G (2020) Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min 10(1):1\u201320","journal-title":"Soc Netw Anal Min"},{"key":"296_CR50","doi-asserted-by":"publisher","first-page":"15129","DOI":"10.1007\/s00521-021-06743-8","volume":"34","author":"DK Jain","year":"2022","unstructured":"Jain DK, Kumar A, Shrivastava A (2022) CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams. Neural Comput Appl 34:15129\u201315140","journal-title":"Neural Comput Appl"},{"issue":"1","key":"296_CR51","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s00521-021-06450-4","volume":"34","author":"V Jain","year":"2022","unstructured":"Jain V, Kaliyar RK, Goswami A, Narang P, Sharma Y (2022) AENeT: an attention-enabled neural architecture for fake news detection using contextual features. Neural Comput Appl 34(1):771\u2013782. https:\/\/doi.org\/10.1007\/s00521-021-06450-4","journal-title":"Neural Comput Appl"},{"key":"296_CR52","doi-asserted-by":"crossref","unstructured":"Jaiswal AK, Srivastava R (2019) Image splicing detection using deep residual network. In: Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE)","DOI":"10.2139\/ssrn.3351072"},{"key":"296_CR53","doi-asserted-by":"crossref","unstructured":"Jeon H, Bang Y, Woo SS (2020) Fdftnet: facing off fake images using fake detection fine-tuning network. In: IFIP international conference on ICT systems security and privacy protection, pp 416\u2013430","DOI":"10.1007\/978-3-030-58201-2_28"},{"key":"296_CR54","doi-asserted-by":"publisher","first-page":"22626","DOI":"10.1109\/ACCESS.2021.3056079","volume":"9","author":"T Jiang","year":"2021","unstructured":"Jiang T, Li JP, Haq AU, Saboor A, Ali A (2021) A novel stacking approach for accurate detection of fake news. IEEE Access 9:22626\u201322639","journal-title":"IEEE Access"},{"key":"296_CR55","doi-asserted-by":"crossref","unstructured":"Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on Multimedia, pp 795\u2013816","DOI":"10.1145\/3123266.3123454"},{"key":"296_CR56","unstructured":"Jindal S, Sood R, Singh R, Vatsa M, Chakraborty T (2020) Newsbag: a multimodal benchmark dataset for fake news detection. In: CEUR Workshop Proc, vol 2560, pp 138\u2013145"},{"key":"296_CR57","doi-asserted-by":"crossref","unstructured":"Kaliyar RK, Dash P (2021) Rueval20: improving rumour detection on social media using a deep convolutional neural network. In: 8th ACM IKDD CODS and 26th COMAD, p 439","DOI":"10.1145\/3430984.3431070"},{"key":"296_CR58","doi-asserted-by":"crossref","unstructured":"Kaliyar RK, Goswami A, Narang P (2019) Multiclass fake news detection using ensemble machine learning. In: 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp 103\u2013107","DOI":"10.1109\/IACC48062.2019.8971579"},{"key":"296_CR59","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.cogsys.2019.12.005","volume":"61","author":"RK Kaliyar","year":"2020","unstructured":"Kaliyar RK, Goswami A, Narang P, Sinha S (2020) FNDNet-a deep convolutional neural network for fake news detection. Cogn Syst Res 61:32\u201344","journal-title":"Cogn Syst Res"},{"key":"296_CR60","doi-asserted-by":"crossref","unstructured":"Kalsnes B (2018) Fake news. In: Oxford Research Encyclopedia of Communication","DOI":"10.1093\/acrefore\/9780190228613.013.809"},{"key":"296_CR61","doi-asserted-by":"crossref","unstructured":"Kazemi A, Garimella K, Gaffney D, Hale SA (2021) Claim matching beyond English to scale global fact-checking. arXiv preprint arXiv:2106.00853","DOI":"10.18653\/v1\/2021.acl-long.347"},{"key":"296_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2021.103112","volume":"190","author":"T Khan","year":"2021","unstructured":"Khan T, Michalas A, Akhunzada A (2021) Fake news outbreak 2021: can we stop the viral spread? J Netw Comput Appl 190:103112","journal-title":"J Netw Comput Appl"},{"key":"296_CR63","doi-asserted-by":"crossref","unstructured":"Khanam Z, Alwasel B, Sirafi H, Rashid M (2021) Fake news detection using machine learning approaches. In: IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol 1099, p 012040","DOI":"10.1088\/1757-899X\/1099\/1\/012040"},{"key":"296_CR64","doi-asserted-by":"crossref","unstructured":"Khattar D, Goud JS, Gupta M, Varma V (2019) Mvae: multimodal variational autoencoder for fake news detection. In: The world wide web conference, pp 2915\u20132921","DOI":"10.1145\/3308558.3313552"},{"key":"296_CR65","unstructured":"Kim S, Breen J, Dudkina E, Poloni F, Crisostomi E (2022) On the use of Markov chains for epidemic modeling on networks. arXiv preprint arXiv:2207.02737"},{"key":"296_CR66","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neucom.2022.01.096","volume":"496","author":"B Koloski","year":"2022","unstructured":"Koloski B, Perdih TS, Robnik-\u0160ikonja M, Pollak S, \u0160krlj B (2022) Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomputing 496:208\u2013226","journal-title":"Neurocomputing"},{"key":"296_CR67","doi-asserted-by":"crossref","unstructured":"Kumar A, Aggarwal N, Kumar S (2022) SIRA: a model for propagation and rumor control with epidemic spreading and immunization for healthcare 5.0. Soft Comput 1\u201314","DOI":"10.1007\/s00500-022-07397-x"},{"key":"296_CR68","doi-asserted-by":"crossref","unstructured":"Kumar N, Pranav P, Nirney V, Geetha V (2021) Deepfake image detection using CNNs and transfer learning. In: 2021 International Conference on Computing, Communication and Green Engineering (CCGE), pp 1\u20136","DOI":"10.1109\/CCGE50943.2021.9776410"},{"key":"296_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115412","volume":"184","author":"R Kumari","year":"2021","unstructured":"Kumari R, Ekbal A (2021) Amfb: attention based multimodal factorized bilinear pooling for multimodal fake news detection. Expert Syst Appl 184:115412","journal-title":"Expert Syst Appl"},{"key":"296_CR70","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-90087-8_1","volume-title":"Combating fake news with computational intelligence techniques","author":"M Lahby","year":"2022","unstructured":"Lahby M, Aqil S, Yafooz W, Abakarim Y (2022) Online fake news detection using machine learning techniques: a systematic mapping study. In: Lahby M, Pathan ASK, Maleh Y, Yafooz WMS (eds) Combating fake news with computational intelligence techniques. Springer, Cham, pp 3\u201337"},{"issue":"4","key":"296_CR71","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s00607-021-01013-w","volume":"104","author":"O Lampridis","year":"2022","unstructured":"Lampridis O, Karanatsiou D, Vakali A (2022) Manifesto: a human-centric explainable approach for fake news spreaders detection. Computing 104(4):717\u2013739","journal-title":"Computing"},{"key":"296_CR72","doi-asserted-by":"publisher","first-page":"3455","DOI":"10.1109\/TMM.2021.3098988","volume":"24","author":"P Li","year":"2021","unstructured":"Li P, Sun X, Yu H, Tian Y, Yao F, Xu G (2021) Entity-oriented multi-modal alignment and fusion network for fake news detection. IEEE Trans Multimedia 24:3455\u20133468","journal-title":"IEEE Trans Multimedia"},{"key":"296_CR73","doi-asserted-by":"crossref","unstructured":"Liu L, Roche DS, Theriault A, Yerukhimovich A (2021) Fighting fake news in encrypted messaging with the fuzzy anonymous complaint tally system (facts). arXiv preprint arXiv:2109.04559","DOI":"10.14722\/ndss.2022.23109"},{"key":"296_CR74","doi-asserted-by":"crossref","unstructured":"Ma K, Tang C, Zhang W, Cui B, Ji K, Chen Z, Abraham A (2022) DC-CNN: dual-channel convolutional neural networks with attention-pooling for fake news detection. Appl Intell 1\u201316","DOI":"10.1007\/s10489-022-03910-9"},{"key":"296_CR75","doi-asserted-by":"crossref","unstructured":"Mahfoudi G, Tajini B, Retraint F, Morain-Nicolier F, Dugelay JL, Marc P (2019) DEFACTO: image and face manipulation dataset. In: 2019 27Th european signal processing conference (EUSIPCO), IEEE, pp 1\u20135","DOI":"10.23919\/EUSIPCO.2019.8903181"},{"key":"296_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112986","volume":"153","author":"P Meel","year":"2020","unstructured":"Meel P, Vishwakarma DK (2020) Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl 153:112986","journal-title":"Expert Syst Appl"},{"key":"296_CR77","doi-asserted-by":"crossref","unstructured":"Mengoni P, Yang J (2022) Empowering COVID-19 fact-checking with extended knowledge graphs. In: International Conference on Computational Science and Its Applications, pp 138\u2013150","DOI":"10.1007\/978-3-031-10536-4_10"},{"key":"296_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1575365","volume":"2022","author":"S Mishra","year":"2022","unstructured":"Mishra S, Shukla P, Agarwal R (2022) Analyzing machine learning enabled fake news detection techniques for diversified datasets. Wirel Commun Mobile Comput 2022:1\u201318","journal-title":"Wirel Commun Mobile Comput"},{"key":"296_CR79","unstructured":"Mishra S, Suryavardan S, Bhaskar A, Chopra P, Reganti A, Patwa P, Das A, Chakraborty T, Sheth A, Ekbal A, et\u00a0al. (2022) Factify: a multi-modal fact verification dataset. In: Proceedings of the First Workshop on Multimodal Fact-Checking and Hate Speech Detection (DE-FACTIFY)"},{"key":"296_CR80","doi-asserted-by":"crossref","unstructured":"Mohamad\u00a0Nezami O, Dras M, Anderson P, Hamey L (2019) Face-cap: image captioning using facial expression analysis. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10\u201314, 2018, Proceedings, Part I 18, Springer, pp 226\u2013240","DOI":"10.1007\/978-3-030-10925-7_14"},{"key":"296_CR81","unstructured":"Monti F, Frasca F, Eynard D, Mannion D, Bronstein MM (2019) Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673"},{"key":"296_CR82","doi-asserted-by":"publisher","first-page":"156151","DOI":"10.1109\/ACCESS.2021.3129329","volume":"9","author":"MF Mridha","year":"2021","unstructured":"Mridha MF, Keya AJ, Hamid MA, Monowar MM, Rahman MS (2021) A comprehensive review on fake news detection with deep learning. IEEE Access 9:156151\u2013156170","journal-title":"IEEE Access"},{"key":"296_CR83","unstructured":"Nakamura K, Levy S, Wang WY (2019) r\/fakeddit: a new multimodal benchmark dataset for fine-grained fake news detection. arXiv preprint arXiv:1911.03854"},{"key":"296_CR84","doi-asserted-by":"crossref","unstructured":"Nakov P, Barr\u00f3n-Cede\u00f1o A, Da\u00a0San\u00a0Martino G, Alam F, Stru\u00df JM, Mandl T, M\u00edguez R, Caselli T, Kutlu M, Zaghouani W, et\u00a0al. (2022) The clef-2022 checkthat! lab on fighting the covid-19 infodemic and fake news detection. In: European Conference on Information Retrieval, pp 416\u2013428","DOI":"10.1007\/978-3-030-99739-7_52"},{"key":"296_CR85","doi-asserted-by":"crossref","unstructured":"Nakov P, Barr\u00f3n-Cede\u00f1o A, da\u00a0San\u00a0Martino G, Alam F, Stru\u00df JM, Mandl T, M\u00edguez R, Caselli T, Kutlu M, Zaghouani W, et\u00a0al. (2022) Overview of the clef\u20132022 checkthat! lab on fighting the covid-19 infodemic and fake news detection. In: International Conference of the Cross-Language Evaluation Forum for European Languages, Springer, pp 495\u2013520","DOI":"10.1007\/978-3-031-13643-6_29"},{"key":"296_CR86","doi-asserted-by":"crossref","unstructured":"Nielsen DS, McConville R (2022) Mumin: a large-scale multilingual multimodal fact-checked misinformation social network dataset. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 3141\u20133153","DOI":"10.1145\/3477495.3531744"},{"key":"296_CR87","unstructured":"Oshikawa R, Qian J, Wang WY (2018) A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770"},{"key":"296_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123174","volume":"540","author":"FA Ozbay","year":"2020","unstructured":"Ozbay FA, Alatas B (2020) Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Stat Mech Appl 540:123174","journal-title":"Physica A: Stat Mech Appl"},{"key":"296_CR89","unstructured":"Pankovska E, Schulz K, Rehm G (2022) Suspicious sentence detection and claim verification in the COVID-19 domain. In: Proceedings of the Workshop Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2022). CEUR-WS, Stavanger"},{"key":"296_CR90","doi-asserted-by":"crossref","unstructured":"Parekh Z, Baldridge J, Cer D, Waters A, Yang Y (2020) Crisscrossed captions: extended intramodal and intermodal semantic similarity judgments for MS-COCO. arXiv preprint arXiv:2004.15020","DOI":"10.18653\/v1\/2021.eacl-main.249"},{"key":"296_CR91","unstructured":"Pathak A (2022) An integrated approach towards automated fact-checking. PhD thesis, State University of New York at Buffalo"},{"key":"296_CR92","first-page":"1","volume":"29","author":"T Pavleska","year":"2018","unstructured":"Pavleska T, \u0160kolkay A, Zankova B, Ribeiro N, Bechmann A (2018) Performance analysis of fact-checking organizations and initiatives in Europe: a critical overview of online platforms fighting fake news. Soc Media Converg 29:1\u201328","journal-title":"Soc Media Converg"},{"issue":"10","key":"296_CR93","doi-asserted-by":"publisher","first-page":"13799","DOI":"10.1007\/s11042-022-12290-8","volume":"81","author":"X Peng","year":"2022","unstructured":"Peng X, Xintong B (2022) An effective strategy for multi-modal fake news detection. Multimedia Tools Appl 81(10):13799\u201313822","journal-title":"Multimedia Tools Appl"},{"issue":"5","key":"296_CR94","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.tics.2021.02.007","volume":"25","author":"G Pennycook","year":"2021","unstructured":"Pennycook G, Rand DG (2021) The psychology of fake news. Trends Cogn Sci 25(5):388\u2013402","journal-title":"Trends Cogn Sci"},{"issue":"1","key":"296_CR95","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/17467586.2021.1895263","volume":"15","author":"JA Piazza","year":"2022","unstructured":"Piazza JA (2022) Fake news: the effects of social media disinformation on domestic terrorism. Dyn Asymmetric Confl 15(1):55\u201377","journal-title":"Dyn Asymmetric Confl"},{"key":"296_CR96","unstructured":"Pritzkau A, Blanc O, Geierhos M, Schade U (2022) NLytics at CheckThat! 2022: hierarchical multi-class fake news detection of news articles exploiting the topic structure. Working Notes of CLEF"},{"key":"296_CR97","doi-asserted-by":"crossref","unstructured":"Probierz B, Kozak J, Stefa\u0144ski P, Juszczuk P (2021) Adaptive goal function of ant colony optimization in fake news detection. In: International Conference on Computational Collective Intelligence, pp 387\u2013400","DOI":"10.1007\/978-3-030-88081-1_29"},{"key":"296_CR98","doi-asserted-by":"crossref","unstructured":"Qazi M, Khan MU, Ali M (2020) Detection of fake news using transformer model. In: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp 1\u20136","DOI":"10.1109\/iCoMET48670.2020.9074071"},{"key":"296_CR99","doi-asserted-by":"crossref","unstructured":"Qi P, Cao J, Yang T, Guo J, Li J (2019) Exploiting multi-domain visual information for fake news detection. In: 2019 IEEE international conference on data mining (ICDM), pp 518\u2013527","DOI":"10.1109\/ICDM.2019.00062"},{"issue":"3","key":"296_CR100","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3451215","volume":"17","author":"S Qian","year":"2021","unstructured":"Qian S, Hu J, Fang Q, Xu C (2021) Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection. ACM Trans Multimedia Comput, Commun, Appl (TOMM) 17(3):1\u201323","journal-title":"ACM Trans Multimedia Comput, Commun, Appl (TOMM)"},{"key":"296_CR101","doi-asserted-by":"crossref","unstructured":"Qian S, Wang J, Hu J, Fang Q, Xu C (2021) Hierarchical multi-modal contextual attention network for fake news detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 153\u2013162","DOI":"10.1145\/3404835.3462871"},{"issue":"11","key":"296_CR102","doi-asserted-by":"publisher","first-page":"8132","DOI":"10.1007\/s10489-021-02345-y","volume":"51","author":"C Raj","year":"2021","unstructured":"Raj C, Meel P (2021) Convnet frameworks for multi-modal fake news detection. Appl Intell 51(11):8132\u20138148","journal-title":"Appl Intell"},{"key":"296_CR103","doi-asserted-by":"publisher","first-page":"8132","DOI":"10.1007\/s10489-021-02345-y","volume":"51","author":"C Raj","year":"2021","unstructured":"Raj C, Meel P (2021) Convnet frameworks for multi-modal fake news detection. Appl Intell 51:8132\u20138148","journal-title":"Appl Intell"},{"issue":"4","key":"296_CR104","first-page":"1","volume":"15","author":"S Ramya","year":"2021","unstructured":"Ramya S, Eswari R (2021) Attention-based deep learning models for detection of fake news in social networks. Int J Cogn Inform Nat Intel (IJCINI) 15(4):1\u201325","journal-title":"Int J Cogn Inform Nat Intel (IJCINI)"},{"key":"296_CR105","doi-asserted-by":"crossref","unstructured":"Ramya S, Eswari R (2022) Performance of optimization algorithms in attention-based deep learning model for fake news detection system. In: Proceedings of International Conference on Computational Intelligence, pp 113\u2013126","DOI":"10.1007\/978-981-16-3802-2_9"},{"key":"296_CR106","doi-asserted-by":"publisher","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","volume":"10","author":"MS Rana","year":"2022","unstructured":"Rana MS, Nobi MN, Murali B, Sung AH (2022) Deepfake detection: a systematic literature review. IEEE Access 10:25494\u201325513","journal-title":"IEEE Access"},{"key":"296_CR107","doi-asserted-by":"crossref","unstructured":"Rashkin H, Choi E, Jang JY, Volkova S, Choi Y (2017) 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","DOI":"10.18653\/v1\/D17-1317"},{"key":"296_CR108","doi-asserted-by":"crossref","unstructured":"Rastogi S, Bansal D (2022) A review on fake news detection 3T\u2019s: typology, time of detection, taxonomies. Int J Inf Secur 1\u201336","DOI":"10.1007\/s10207-022-00625-3"},{"issue":"4","key":"296_CR109","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s41060-021-00302-z","volume":"13","author":"S Raza","year":"2022","unstructured":"Raza S, Ding C (2022) Fake news detection based on news content and social contexts: a transformer-based approach. Int J Data Sci Anal 13(4):335\u2013362","journal-title":"Int J Data Sci Anal"},{"key":"296_CR110","unstructured":"Riedel B, Augenstein I, Spithourakis GP, Riedel S (2017) A simple but tough-to-beat baseline for the fake news challenge stance detection task. arXiv preprint arXiv:1707.03264"},{"key":"296_CR111","doi-asserted-by":"publisher","first-page":"30367","DOI":"10.1109\/ACCESS.2022.3159651","volume":"10","author":"D Rohera","year":"2022","unstructured":"Rohera D, Shethna H, Patel K, Thakker U, Tanwar S, Gupta R, Hong WC, Sharma R (2022) A taxonomy of fake news classification techniques: survey and implementation aspects. IEEE Access 10:30367\u201330394","journal-title":"IEEE Access"},{"key":"296_CR112","doi-asserted-by":"publisher","first-page":"17425","DOI":"10.1007\/s00521-021-06328-5","volume":"33","author":"S Sagnika","year":"2021","unstructured":"Sagnika S, Mishra BSP, Meher SK (2021) An attention-based CNN-LSTM model for subjectivity detection in opinion-mining. Neural Comput Appl 33:17425\u201317438","journal-title":"Neural Comput Appl"},{"key":"296_CR113","doi-asserted-by":"crossref","unstructured":"Saji R, Anand SK, Chandavarkar B (2021) Comparing CNNs and GANs for image completion. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp 1\u20137","DOI":"10.1109\/ICCCNT51525.2021.9579725"},{"key":"296_CR114","doi-asserted-by":"crossref","unstructured":"Santia G, Williams J (2018) Buzzface: A news veracity dataset with facebook user commentary and egos. In: Proceedings of the international AAAI conference on web and social media, vol, 12, pp 531\u2013540","DOI":"10.1609\/icwsm.v12i1.14985"},{"key":"296_CR115","unstructured":"Schoenmueller V, Blanchard SJ, Johar GV (2022) Who will share fake-news on twitter? psycholinguistic cues in online post histories discriminate between actors in the misinformation ecosystem. arXiv preprint arXiv:2203.10560"},{"issue":"6","key":"296_CR116","doi-asserted-by":"publisher","first-page":"284","DOI":"10.3390\/info13060284","volume":"13","author":"I Segura-Bedmar","year":"2022","unstructured":"Segura-Bedmar I, Alonso-Bartolome S (2022) Multimodal fake news detection. Information 13(6):284","journal-title":"Information"},{"key":"296_CR117","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.neucom.2022.09.135","volume":"513","author":"JW Seow","year":"2022","unstructured":"Seow JW, Lim MK, Phan RCW, Liu JK (2022) A comprehensive overview of deepfake: generation, detection, datasets, and opportunities. Neurocomputing 513:351\u2013371","journal-title":"Neurocomputing"},{"key":"296_CR118","doi-asserted-by":"crossref","unstructured":"Shahid W, Jamshidi B, Hakak S, Isah H, Khan WZ, Khan MK, Choo KKR (2022) Detecting and mitigating the dissemination of fake news: challenges and future research opportunities. IEEE Trans Comput Soc Syst","DOI":"10.1109\/TCSS.2022.3177359"},{"key":"296_CR119","doi-asserted-by":"crossref","unstructured":"Shao Y, Sun J, Zhang T, Jiang Y, Ma J, Li J (2022) Fake news detection based on multi-modal classifier ensemble. In: Proceedings of the 1st International Workshop on Multimedia AI against Disinformation, pp 78\u201386","DOI":"10.1145\/3512732.3533583"},{"key":"296_CR120","doi-asserted-by":"crossref","unstructured":"Sharma DK, Garg S (2021) IFND: a benchmark dataset for fake news detection. Complex Intell Syst 1\u201321","DOI":"10.1007\/s40747-021-00552-1"},{"issue":"3","key":"296_CR121","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3305260","volume":"10","author":"K Sharma","year":"2019","unstructured":"Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y (2019) Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 10(3):1\u201342","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"6","key":"296_CR122","first-page":"509","volume":"8","author":"U Sharma","year":"2020","unstructured":"Sharma U, Saran S, Patil SM (2020) Fake news detection using machine learning algorithms. Int J Creative Res Thoughts (IJCRT) 8(6):509\u2013518","journal-title":"Int J Creative Res Thoughts (IJCRT)"},{"issue":"5","key":"296_CR123","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1109\/TCSS.2020.3014135","volume":"7","author":"G Shrivastava","year":"2020","unstructured":"Shrivastava G, Kumar P, Ojha RP, Srivastava PK, Mohan S, Srivastava G (2020) Defensive modeling of fake news through online social networks. IEEE Trans Comput Soc Syst 7(5):1159\u20131167","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"296_CR124","doi-asserted-by":"crossref","unstructured":"Shrivastava S, Singh R, Jain C, Kaushal S (2022) A research on fake news detection using machine learning algorithm. In: Smart Systems: Innovations in Computing: Proceedings of SSIC 2021, pp 273\u2013287","DOI":"10.1007\/978-981-16-2877-1_25"},{"issue":"1","key":"296_CR125","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl 19(1):22\u201336","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"296_CR126","doi-asserted-by":"crossref","unstructured":"Shu K, Wang S, Liu H (2019) 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","DOI":"10.1145\/3289600.3290994"},{"issue":"3","key":"296_CR127","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 (2020) 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","journal-title":"Big data"},{"issue":"5","key":"296_CR128","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102618","volume":"58","author":"A Silva","year":"2021","unstructured":"Silva A, Han Y, Luo L, Karunasekera S, Leckie C (2021) Propagation2vec: embedding partial propagation networks for explainable fake news early detection. Inf Process Manag 58(5):102618","journal-title":"Inf Process Manag"},{"key":"296_CR129","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"296_CR130","doi-asserted-by":"publisher","first-page":"21503","DOI":"10.1007\/s00521-021-06086-4","volume":"34","author":"B Singh","year":"2021","unstructured":"Singh B, Sharma DK (2021) Predicting image credibility in fake news over social media using multi-modal approach. Neural Comput Appl 34:21503\u201321517","journal-title":"Neural Comput Appl"},{"key":"296_CR131","doi-asserted-by":"crossref","unstructured":"Singh P, Srivastava R, Rana K, Kumar V (2022) SEMI-FND: stacked ensemble based multimodal inference for faster fake news detection. arXiv preprint arXiv:2205.08159","DOI":"10.1016\/j.eswa.2022.119302"},{"issue":"1","key":"296_CR132","first-page":"3","volume":"72","author":"VK Singh","year":"2021","unstructured":"Singh VK, Ghosh I, Sonagara D (2021) Detecting fake news stories via multimodal analysis. J Am Soc Inf Sci 72(1):3\u201317","journal-title":"J Am Soc Inf Sci"},{"key":"296_CR133","doi-asserted-by":"crossref","unstructured":"Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh S (2019) Spotfake: a multi-modal framework for fake news detection. In: 2019 IEEE fifth international conference on multimedia big data (BigMM), pp 39\u201347","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"296_CR134","doi-asserted-by":"crossref","unstructured":"Singhal S, Shah RR, Kumaraguru P (2022) FactDrill: a data repository of fact-checked social media content to study fake news incidents in India. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 16, pp 1322\u20131331","DOI":"10.1609\/icwsm.v16i1.19384"},{"issue":"4","key":"296_CR135","doi-asserted-by":"publisher","first-page":"546","DOI":"10.58680\/ccc202131441","volume":"72","author":"R Skinnell","year":"2021","unstructured":"Skinnell R (2021) Teaching writing in the (new) era of fake news. Coll Compos Commun 72(4):546\u2013569","journal-title":"Coll Compos Commun"},{"key":"296_CR136","first-page":"44","volume":"45","author":"B Soni","year":"2019","unstructured":"Soni B, Das PK, Thounaojam DM (2019) Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features. J Inf Secur Appl 45:44\u201351","journal-title":"J Inf Secur Appl"},{"issue":"14","key":"296_CR137","first-page":"5072","volume":"100","author":"SE Sorour","year":"2022","unstructured":"Sorour SE, Abdelkader HE (2022) AFND: Arabic fake news detection with an ensemble deep CNN-LSTM model. J Theor Appl Inf Technol 100(14):5072\u20135086","journal-title":"J Theor Appl Inf Technol"},{"key":"296_CR138","doi-asserted-by":"crossref","unstructured":"Sudhakar M, Kaliyamurthie K (2023) Efficient prediction of fake news using novel ensemble technique based on machine learning algorithm. In: Information and Communication Technology for Competitive Strategies (ICTCS 2021), pp 1\u20138","DOI":"10.1007\/978-981-19-0098-3_1"},{"key":"296_CR139","doi-asserted-by":"crossref","unstructured":"Tariq S, Lee S, Kim H, Shin Y, Woo SS (2019) Gan is a friend or foe? A framework to detect various fake face images. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, pp 1296\u20131303","DOI":"10.1145\/3297280.3297410"},{"key":"296_CR140","doi-asserted-by":"crossref","unstructured":"Tian L, Zhang X, Peng M (2020) FakeFinder: twitter fake news detection on mobile. In: Companion Proceedings of the Web Conference 2020, pp 79\u201380","DOI":"10.1145\/3366424.3382706"},{"key":"296_CR141","doi-asserted-by":"crossref","unstructured":"Tiwary T, Mahapatra RP (2022) An accurate generation of image captions for blind people using extended convolutional atom neural network. Multimedia Tools Appl 1\u201330","DOI":"10.1007\/s11042-022-13443-5"},{"key":"296_CR142","unstructured":"Tuan NMD, Minh PQN (2021) Multimodal fusion with BERT and attention mechanism for fake news detection. In: 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), pp 1\u20136"},{"key":"296_CR143","doi-asserted-by":"crossref","unstructured":"Tyagi S, Yadav D (2022) MiniNet: a concise CNN for image forgery detection. Evol Syst 1\u201312","DOI":"10.1007\/s12530-022-09446-0"},{"issue":"5","key":"296_CR144","doi-asserted-by":"publisher","first-page":"2028","DOI":"10.1177\/1461444817712086","volume":"20","author":"CJ Vargo","year":"2018","unstructured":"Vargo CJ, Guo L, Amazeen MA (2018) The agenda-setting power of fake news: a big data analysis of the online media landscape from 2014 to 2016. New Media Soc 20(5):2028\u20132049","journal-title":"New Media Soc"},{"key":"296_CR145","volume-title":"Advances in neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc., New York"},{"key":"296_CR146","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/978-981-19-4831-2_45","volume-title":"Applications of artificial intelligence and machine learning","author":"PK Verma","year":"2022","unstructured":"Verma PK, Agrawal P (2022) PropFND: propagation based fake news detection. In: Unhelker B, Pandey HM, Raj G (eds) Applications of artificial intelligence and machine learning. Springer, Singapore, pp 557\u2013568"},{"key":"296_CR147","unstructured":"Vijjali R, Potluri P, Kumar S, Teki S (2020) Two stage transformer model for COVID-19 fake news detection and fact checking. arXiv preprint arXiv:2011.13253"},{"key":"296_CR148","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.cogsys.2019.07.004","volume":"58","author":"DK Vishwakarma","year":"2019","unstructured":"Vishwakarma DK, Varshney D, Yadav A (2019) Detection and veracity analysis of fake news via scrapping and authenticating the web search. Cogn Syst Res 58:217\u2013229","journal-title":"Cogn Syst Res"},{"issue":"1","key":"296_CR149","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s42438-019-00066-7","volume":"2","author":"A Wagener","year":"2020","unstructured":"Wagener A (2020) Hypernarrativity, storytelling, and the relativity of truth: digital semiotics of communication and interaction. Postdigital Sci Educ 2(1):147\u2013169","journal-title":"Postdigital Sci Educ"},{"key":"296_CR150","unstructured":"Wang J, Sun Z, Wang J, Wu H, Hu X (2020) A two-stage attention-based model for fake news detection. arXiv preprint arXiv:2004.14420"},{"issue":"3","key":"296_CR151","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.3390\/app12031093","volume":"12","author":"J Wang","year":"2022","unstructured":"Wang J, Mao H, Li H (2022) FMFN: fine-grained multimodal fusion networks for fake news detection. Appl Sci 12(3):1093","journal-title":"Appl Sci"},{"key":"296_CR152","doi-asserted-by":"publisher","first-page":"42527","DOI":"10.1007\/s11042-021-11592-7","volume":"81","author":"J Wang","year":"2022","unstructured":"Wang J, Zeng K, Ma B, Luo X, Yin Q, Liu G, Jha SK (2022) GAN-generated fake face detection via two-stream CNN with PRNU in the wild. Multimedia Tools Appl 81:42527\u201342545","journal-title":"Multimedia Tools Appl"},{"key":"296_CR153","doi-asserted-by":"crossref","unstructured":"Wang WY (2017) \u201cliar, liar pants on fire\u201d: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648","DOI":"10.18653\/v1\/P17-2067"},{"key":"296_CR154","doi-asserted-by":"crossref","unstructured":"Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) 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","DOI":"10.1145\/3219819.3219903"},{"key":"296_CR155","doi-asserted-by":"crossref","unstructured":"Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In: KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","DOI":"10.1145\/3219819.3219903"},{"key":"296_CR156","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1109\/LSP.2022.3181893","volume":"29","author":"P Wei","year":"2022","unstructured":"Wei P, Wu F, Sun Y, Zhou H, Jing XY (2022) Modality and event adversarial networks for multi-modal fake news detection. IEEE Signal Process Lett 29:1382\u20131386","journal-title":"IEEE Signal Process Lett"},{"issue":"11","key":"296_CR157","doi-asserted-by":"publisher","first-page":"39","DOI":"10.22215\/timreview\/1282","volume":"9","author":"M Westerlund","year":"2019","unstructured":"Westerlund M (2019) The emergence of deepfake technology: a review. Technol Innov Manag Rev 9(11):39\u201352","journal-title":"Technol Innov Manag Rev"},{"key":"296_CR158","doi-asserted-by":"crossref","unstructured":"Wright D, Wadden D, Lo K, Kuehl B, Cohan A, Augenstein I, Wang LL (2022) Generating scientific claims for zero-shot scientific fact checking. arXiv preprint arXiv:2203.12990","DOI":"10.18653\/v1\/2022.acl-long.175"},{"key":"296_CR159","doi-asserted-by":"crossref","unstructured":"Wu K, Yang S, Zhu KQ (2015) False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st international conference on data engineering, pp 651\u2013662","DOI":"10.1109\/ICDE.2015.7113322"},{"issue":"5","key":"296_CR160","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102610","volume":"58","author":"J Xue","year":"2021","unstructured":"Xue J, Wang Y, Tian Y, Li Y, Shi L, Wei L (2021) Detecting fake news by exploring the consistency of multimodal data. Inf Process Manag 58(5):102610","journal-title":"Inf Process Manag"},{"key":"296_CR161","doi-asserted-by":"crossref","unstructured":"Yang C, Xu B, Khan JY, Uddin G, Han D, Yang Z, Lo D (2022) Aspect-based api review classification: How far can pre-trained transformer model go?. In: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp 385\u2013395","DOI":"10.1109\/SANER53432.2022.00054"},{"key":"296_CR162","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.dcan.2022.07.010","volume":"8","author":"J Yang","year":"2022","unstructured":"Yang J, Xiao S, Lv Z (2022) Protecting the trust and credibility of data by tracking forgery trace based on GANs. Digit Commun Netw 8:877\u2013884","journal-title":"Digit Commun Netw"},{"key":"296_CR163","unstructured":"Yang Z, Ma J, Chen H, Lin H, Luo Z, Chang Y (2022) A coarse-to-fine cascaded evidence-distillation neural network for explainable fake news detection. arXiv preprint arXiv:2209.14642"},{"key":"296_CR164","doi-asserted-by":"publisher","first-page":"132818","DOI":"10.1109\/ACCESS.2021.3113981","volume":"9","author":"L Ying","year":"2021","unstructured":"Ying L, Yu H, Wang J, Ji Y, Qian S (2021) Fake news detection via multi-modal topic memory network. IEEE Access 9:132818\u2013132829","journal-title":"IEEE Access"},{"key":"296_CR165","doi-asserted-by":"publisher","first-page":"132363","DOI":"10.1109\/ACCESS.2021.3114093","volume":"9","author":"L Ying","year":"2021","unstructured":"Ying L, Yu H, Wang J, Ji Y, Qian S (2021) Multi-level multi-modal cross-attention network for fake news detection. IEEE Access 9:132363\u2013132373","journal-title":"IEEE Access"},{"key":"296_CR166","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.patrec.2021.11.026","volume":"153","author":"C Yu","year":"2022","unstructured":"Yu C, Wang W (2022) Fast transformation of discriminators into encoders using pre-trained GANs. Pattern Recogn Lett 153:92\u201399","journal-title":"Pattern Recogn Lett"},{"key":"296_CR167","unstructured":"Zhan J, Li X, Wang J, Liu H, Huang S (2019) A multi-head attention neural network model for fake news detection. arXiv preprint arXiv:1910.09871"},{"key":"296_CR168","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/TMM.2021.3065498","volume":"24","author":"H Zhang","year":"2021","unstructured":"Zhang H, Qian S, Fang Q, Xu C (2021) Multi-modal meta multi-task learning for social media rumor detection. IEEE Trans Multimedia 24:1449\u20131459","journal-title":"IEEE Trans Multimedia"},{"key":"296_CR169","doi-asserted-by":"crossref","unstructured":"Zhang T, Wang D, Chen H, Zeng Z, Guo W, Miao C, Cui L (2020) BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9206973"},{"key":"296_CR170","doi-asserted-by":"publisher","unstructured":"Zhou K, Shu C, Li B, Lau JH (2019a) Early rumour detection. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1 (Long and Short Papers), pp 1614\u20131623, Association for Computational Linguistics, Minneapolis, Minnesota, https:\/\/doi.org\/10.18653\/v1\/N19-1163, https:\/\/aclanthology.org\/N19-1163","DOI":"10.18653\/v1\/N19-1163"},{"key":"296_CR171","unstructured":"Zhou X, Zafarani R (2018) Fake news: a survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315 2"},{"key":"296_CR172","doi-asserted-by":"crossref","unstructured":"Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 53(5):1\u201340","DOI":"10.1145\/3395046"},{"key":"296_CR173","doi-asserted-by":"crossref","unstructured":"Zhou X, Wu J, Zafarani R (2020) Safe: similarity-aware multi-modal fake news detection (2020). Preprint arXiv:2003.04981","DOI":"10.1007\/978-3-030-47436-2_27"},{"issue":"2","key":"296_CR174","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3161603","volume":"51","author":"A Zubiaga","year":"2018","unstructured":"Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R (2018) Detection and resolution of rumours in social media: a survey. ACM Comput Surv (CSUR) 51(2):1\u201336","journal-title":"ACM Comput Surv (CSUR)"}],"container-title":["International Journal of Multimedia Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00296-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13735-023-00296-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00296-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T14:14:14Z","timestamp":1701526454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13735-023-00296-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":174,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["296"],"URL":"https:\/\/doi.org\/10.1007\/s13735-023-00296-3","relation":{},"ISSN":["2192-6611","2192-662X"],"issn-type":[{"value":"2192-6611","type":"print"},{"value":"2192-662X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,23]]},"assertion":[{"value":"17 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"For this type of study, formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"28"}}