The widespread adoption of social media platforms has given rise to a plethora of multimedia content circulating across these networks. The open and unrestricted sharing of information on these platforms has created an environment where data dissemination on the internet is unbounded by considerations of its credibility. Within the realm of social media, the propagation of misinformation, often in the form of false information or rumors, is a prevalent issue. Such unverified information can have dire consequences. Despite the extensive usage of social media platforms, their unregulated nature frequently fosters the generation and diffusion of unverified and speculative content. Consequently, the automatic detection of rumors on social media platforms has emerged as a crucial research domain within the field of social analysis. With this same motivation in mind, this article places its focus on datasets and cutting-edge methodologies employed for rumor detection. Furthermore, it delves into both supervised and unsupervised approaches, as well as delving into the application of deep learning techniques for word recognition.

1. Cao,Juanetal.(2018).“AutomaticRumorDetectionon Microblogs: A Survey.” arXiv preprint arXiv:1807.03505.
2. Nipah Virus, https://mumbaimirror.indiatimes.com/news/india/nipah- virus/
3. BMRCL,https://timesofindia.indiatimes.com/
4. Zubiaga,Arkaitzetal.(2018).“Detectionand Resolution of Rumours in Social Media: A Survey.” ACM Computing Surveys (CSUR) 51(2): 32.
5. Kwon,Sejeongetal.(2013).“Prominent Features of Rumor Propagation in Online Social Media.” In2013 IEEE 13th International Conference on Data Mining, 1103–8.
6. Boididou, Christinaetal.(2014).“Challenges of Computational Verification in Social Multimedia.” In Proceedingsofthe23rdInternationalConferenceonWorld Wide Web, 743–48.
7. Ma,Jingetal.(2016).“Detecting Rumors from Microblogs with Recurrent Neural Networks.” In Ijcai, 3818–24.
8. Castillo,Carlos,MarceloMendoza,andBarbaraPoblete. (2011). “Information Credibility on Twitter.” InProceedingsofthe20th International Conferenceon World Wide Web, 675–84.
9. Derczynski, Leon etal. (2017).“SemEval-2017 Task8: RumourEval:Determining Rumour Veracity and Support for Rumours.” arXiv preprint arXiv:1704.05972.
10. Jin,Zhiweietal.(2017).“Multimodal Fusion with Recurrent Neural Networks for Rumor Detection On Microblogs.” InProceedings of the 25th ACM International Conference on Multimedia, 795–816.
11. Wang,WilliamYang.(2017).“Liar,LiarPantson Fire’: A New Benchmark Dataset for Fake News Detection.”arXivpreprintarXiv:1705.00648.
12. Ma,Jing,WeiGao,andKam-FaiWong.(2018). “Detect Rumor and Stance Jointly by Neural Multi-TaskLearning.”InCompanionoftheTheWebConference 2018 on The Web Conference 2018, 585–93.
13. Yang,Fan,YangLiu,XiaohuiYu,andMinYang. (2012). “Automatic Detection of Rumor on Sina Weibo.”In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, 13.
14. Greenhill,KellyM,andBenOppenheim.(2017). “Rumor Has It: The Adoption of Unverified InformationinConflictZones.”InternationalStudies Quarterly 61(3): 660–76
15. Dayani,Raveena,NikitaChhabra,TarunaKadian,and RishabhKaushal. (2015). “Rumor Detection in Twitter:AnAnalysisinRetrospect.”In2015IEEE International Conference on Advanced Networks and Teecommuncations Systems (ANTS), 1–3.
16. Alzanin,SamahM,andAqilMAzmi.(2018).“Detecting Rumors in Social Media: A Survey.” Procediacomputerscience142:294–300.
17. Hamidian, Sardar, and Mona T Diab. (2015). “Rumor Detection and Classification for Twitter Data.” InProceedingsoftheFifthInternationalConferenceon Social Media Technologies, Communication, andInformatics(SOTICS),71–77.
18. Hall,Marketal.(2009).“TheWEKA DataMining Software: An Update.” ACM SIGKDD explorations newsletter 11(1): 10–18.
19. Kwon,Sejeong,MeeyoungCha,andKyominJung. 2017. “Rumor Detection over Varying Time Windows.”PloSone12(1):e0168344
20. Takahashi,Tetsuro,andNobuyukiIgata.(2012). “Rumor Detection on Twitter.” In The 6th InternationalConferenceonSoftComputingandIntelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 452–57.
21. Chang,Cheng,YihongZhang,ClaudiaSzabo,and Quan Z Sheng. (2016). “Extreme User and Political Rumor Detection on Twitter.” In International Conference on Advanced Data Mining and Applications, 751–63.
22. Jain,Suchita,VanyaSharma,andRishabhKaushal. (2016). “Towards Automated Real-Time DetectionofMisinformationonTwitter.”In2016International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015–20.
23. Chen, Weiling, Chai Kiat Yeo, Chiew Tong Lau, and Bu Sung Lee. (2016). “Behavior Deviation: An Anomaly Detection View of Rumor Preemption.” In 2016 IEEE 7th Annual Information Technology,ElectronicsandMobileCommunicationConference (IEMCON), , 1–7.
24. Pathak, Ajeet Ram, ManjushaPandey, and SiddharthRautaray. (2018). “Application of DeepLearningforObjectDetection.”ProcediaComput.Sci132: 1706–17.
25. Ma, Jing, Wei Gao, and Kam-Fai Wong. (2018). “Rumor Detection on Twitter with Tree-Structured Recursive Neural Networks.” In Proceedings of the 56th Annual Meeting of the Association forComputationalLinguistics(Volume1:LongPapers),1980– 89.
26. Ruchansky, Natali, SungyongSeo, and Yan Liu. (2017). “Csi: A Hybrid Deep Model for Fake News Detection.”InProceedingsofthe2017ACMonConference on Information and Knowledge Management,797–806
27. Yu,Fengetal.(2017).“AConvolutionalApproachfor Misinformation Identification.”
28. Wang,Yaqingetal.(2018).“Eann:EventAdversarial Neural Networks for Multi-Modal Fake News Detection.”In Proceedings of the 24th ACM SIGKDD Detection.” In 2015 12th Web Information System and Application Conference (WISA), 53–58.
29. Wang, Shihan, and Takao Terano. (2015). “Detecting Rumor Patterns in Streaming Social Media.” In 2015 IEEE International Conference on Big Data (Big Data), 2709–15.
30. Wu,Ke,SongYang,andKennyQZhu.(2015).“False Rumors Detection on SinaWeibo by Propagation Structures.”In 2015 IEEE 31st International Conference on Data Engineering, 651–62.
31. Granik, Mykhailo, and VolodymyrMesyura. (2017). “Fake News Detection Using Naive BayesClassifier.”In2017IEEEFirstUkraineConferenceon Electrical and Computer Engineering (UKRCON),900–903.
32. InternationalConferenceonKnowledgeDiscovery & Data Mining, 849–57.
33. Cai, Guoyong, HaoWu, andRuiLv. (2014). “Rumors Detection in Chinese via Crowd Responses.” In Proceedingsofthe2014IEEE/ACMInternationalConference on Advances in Social Networks AnalysisandMining,912–17.
34. Liu,Yang,andSonghuaXu.(2016).“Detecting Rumors through Modeling Information PropagationNetworksinaSocialMediaEnvironment.”IEEETransactions on computational social systems 3(2):46–62.
35. Chen,Weilingetal.(2018).“UnsupervisedRumor Detection Based on Users’ Behaviors Using NeuralNetworks.”PatternRecognitionLetters105: 226–33.
36. Ajao, Oluwaseun, DeepayanBhowmik, and ShahrzadZargari. (2018). “Fake News Identification onTwitterwithHybridCnnandRnnModels.”InProceedings of the 9th International Conference on SocialMediaandSociety,226–30.
37. Thota,Aswini,PriyankaTilak,SimratAhluwalia,and NibratLohia. (2018). “Fake News Detection: ADeepLearningApproach.”SMUDataScienceReview1(3): 10.
38. Boididou,Christinaetal.(2015).“Verifying Multimedia Use at MediaEval 2015.” In MediaEval,.
39. Yang,Fan,YangLiu,XiaohuiYu,andMinYang. (2012). “Automatic Detection of Rumor on Sina Weibo.”In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, 13.
40. Jin, Fang et al. (2013). “Epidemiological Modeling of News and Rumors on Twitter.” In Proceedingsofthe7thWorkshoponSocialNetworkMiningand Analysis, 8.
41. Kwon, Sejeong et al. (2013). “Prominent Features of Rumor Propagation in Online Social Media.” In2013 IEEE 13th International Conference on Data Mining, 1103–8.
42. Wang,De.(2014).“AnalysisandDetectionofLow Quality Information in Social Networks.” In 2014 IEEE30thInternationalConferenceonDataEngineering Workshops, 350–54.
43. Liu,Xiaomoetal.(2015).“Real-TimeRumor Debunking on Twitter.” In Proceedings of the 24th ACM Internationalon Conference on Information and Knowledge Management, 1867–70.
44. Yang,Zhifanetal.(2015).“EmergingRumor Identification for Social Media with Hot Topic.
45. Sharma, T., Prasad, S.K. Enhancing cybersecurity in IoT networks: SLSTM-WCO algorithm for anomaly detection. Peer-to-Peer Netw. Appl. 17, 2237–2258(2024). https://doi.org/10.1007/s12083-024-01712-z
46. Sharma, T., Pal, A., Kaushik A., Yadav, A., Chitragupt, A., A Survey on Flood Prediction Analysis Based on ML Algorithm using Data Science Methodology 2022 IEEE Delhi Section Conference, DELCON 2022.
47. Sharma, T., Prasad, S.K., Sharma, V., Research Challenges of Block chain in 6G Network ,2022 IEEE Delhi Section Conference, DELCON 2022.
48. Prasad, S.K., Sharma, T., Performance Comparison of Multipath routing Protocols for Mobile Ad hoc Network International Journal of Systems, Control and Communications, 2022, 13,(1), pp.82-98.
49. Sharma, T., Kumar, V., Congestion Aware Link Cost Routing for MANETS, International Journal of Computer Networks and Communications, 2016, 8(3), pp.167-179.

False Information Detection Machine Learning