Due to recent mobile technology advances, consumers have 24* 7 accesses to social networks. With regard to knowledge gaps, the dissemination of misinformation is closely linked, particularly when the data is published slowly, often as unverified data. A significant investigation is done in online social media, particularly micro-blogging websites, automatically detect rumors. Recent research on the follow-up of disinformation in social media has explored such terminology. This article will present an overview of social media rumor detection research including various types of rumor classification available in order to recognize the rumor and class text. In this survey paper we will also highlight the features of classification algorithms like Naïve Bayes, Support Vector Machine, Logistic Regression and K-Nearest Neighbor.

Rumor detection, social networks, machine Learning, fake, NLP

[1] K. Ali, H. Dong, A. Bouguettaya, A. Erradi, and R. Hadjidj, “Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework,” in Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017, 2017.
[2] H. Ahmed, I. Traore, and S. Saad, “Detecting opinion spams and fake news using text classification,” Secur. Priv., 2018.
[3] M. Granik and V. Mesyura, “Fake news detection using naive Bayes classifier,” in 2017 IEEE 1st Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 - Proceedings, 2017.
[4] M. Z. Asghar, A. Khan, F. Khan, and F. M. Kundi, “RIFT: A Rule Induction Framework for Twitter Sentiment Analysis,” Arab. J. Sci. Eng., 2018.
[5] G. W. Allport and L. Postman, “An analysis of rumor,” Public Opin. Q., 1946.
[6] H. Dunn and C. Allen, “Rumors, urban legends and internet hoaxes,” Proc. Annu. Meet. …, 2005.
[7] N. DiFonzo and P. Bordia, “Rumor, gossip and urban legends,” Diogenes. 2007.
[8] M. Rezwanul, A. Ali, and A. Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM,” Int. J. Adv. Comput. Sci. Appl., 2017.
[9] A. Zubiaga, M. Liakata, and R. Procter, “Exploiting context for rumour detection in social media,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017.
[10] W. Etaiwi and G. Naymat, “The Impact of applying Different Preprocessing Steps on Review Spam Detection,” in Procedia Computer Science, 2017.
[11] S. Kotsiantis, E. Koumanakos, D. Tzelepis, and V. Tampakas, “Predicting fraudulent financial statements with machine learning techniques,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006.
[12] B. Ma, D. Lin, and D. Cao, “Content representation for microblog rumor detection,” in Advances in Intelligent Systems and Computing, 2017.
[13] A. Majumdar and I. Bose, “Detection of financial rumors using big data analytics: the case of the Bombay Stock Exchange,” J. Organ. Comput. Electron. Commer., 2018.
[14] S. Tschiatschek, A. Singla, M. Gomez Rodriguez, A. Merchant, and A. Krause, “Fake News Detection in Social Networks via Crowd Signals,” 2018.
[15] S. Hamidian and M. Diab, “Rumor Identification and Belief Investigation on Twitter,” 2016.
[16] Rajit Nair, Vaibhav Jain, Amit Bhagat, Ratish Agarwal, “An Efficient Approach for Sentiment Analysis Using Regression Analysis Technique,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.161-165, 2019..
[17] P. Donovan, “How idle is idle talk? One hundred years of rumor research,” Diogenes. 2007.
[18] V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei, “Rumor has it: Identifying misinformation in microblogs,” in EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2011.
[19] P. Bordia, “Studying verbal interaction on the Internet: The case of rumor transmission research,” Behav. Res. Methods, Instruments, Comput., 1996.
[20] M. Takayasu, K. Sato, Y. Sano, K. Yamada, W. Miura, and H. Takayasu, “Rumor diffusion and convergence during the 3.11 Earthquake: A twitter case study,” PLoS One, 2015.
[21] C. Castillo, M. Mendoza, and B. Poblete, “Predicting information credibility in time-sensitive social media,” Internet Res., 2013.
[22] K. Chai, H. T. Hn, and H. L. Cheiu, “Naive-Bayes Classification Algorithm,” Proc. 25th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2002.
[23] P. Cunningham and S. J. Delany, “K -Nearest Neighbour Classifiers,” Mult. Classif. Syst., 2007.
[24] C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., 1995.
[25] S. R. Safavian and D. Landgrebe, “A Survey of Decision Tree Classifier Methodology,” IEEE Trans. Syst. Man Cybern., 1991.