Rumour Source Identification Using Machine Learning Algorithms


  • Raja Kumari Mukiri Mukiri Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  • Vijaya Babu Burra Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India


Real-time rumour prediction, social media, Support Vector Machine


Rumour is one of the most prevalent types of false information on social media, and it should be prevented as soon as possible to avoid significant repercussions. We provide another task termed "talk expectation," which assesses the likelihood of a report appearing from an online entertainment stream turning into gossip from now on. This is largely owing to the ease with which knowledge can be promptly disseminated to the general population, as well as the low cost of access. To overcome this challenge, several studies employ social media rumour detection. Four machine learning techniques—LR, SVM, RF, and XGBoost—were evaluated on various rumour debunking microblogs. In our technique for forecasting rumours, we mix content-based and novelty-based elements. In comparison to existing models, the suggested rumour prediction model outperforms them all. The SVM rumour Prediction Model technique was chosen because it enhanced accuracy by 89 percent, recall by 64 percent, and F-measure by 85 percent. The experiment findings revealed that the suggested approach will be beneficial for evading social problems produced by pieces of gossip in online entertainment.


Download data is not yet available.


S.Petrovic, M. Osborne, R. McCreadie, et al., “Can Twitter replace Newswire for breaking news?”, Proceedings of the Seventh International AAAI Conference on Weblogs and social media., Massachusetts, USA, pp.713–716, 2013.

Greenhill, Kelly M, and Ben Oppenheim. (2017). “Rumour Has It: The Adoption of Unverified Information in Conflict Zones.” International Studies Quarterly 61(3): 660–76

Nipah Virus,



Wen, Jiang, Xiang, Yu, Zhou, & Jia, 2014” To Shut Them Up or to Clarify: Restraining the Spread of Rumours in Online Social Networks”


Z. Zhao, P. Resnick and Q. Mei, “Enquiring minds: Early detection of rumours in social media from enquiry posts”, Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.1395–1405, 2015.

X. Liu, A. Nourbakhsh, Q. Li, R. Fang, et al., “Real-time rumour debunking on twitter”, Proceedings of the 24th ACM International Conference on Information and Knowledge Management, ACM, Melbourne, Australia, pp.1867–1870, 2015.

X. Zhou, J. Cao, Z. Jin, et al., “Realtime news certification system on Sina weibo”, Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.983–988, 2015

F. Yang, Y. Liu, X. Yu, and M. Yang, ‘‘Automatic detection of rumour on Sina Weibo,’’ in Proc. ACM SIGKDD Workshop Mining Data Semantics” New York, NY, USA, 2012, pp. 1–7.

G. Liang, W. He, C. Xu, L. Chen, and J. Zeng, ‘‘Rumour identification in microblogging systems based on users’ behaviour,’’ IEEE Trans. Computat. Social Syst., vol. 2, no. 3, pp. 99–108, Sep. 2015.

S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, ‘‘Prominent features of rumour propagation in online social media,’’ in Proc. IEEE 13th Int. Conf-Data Mining, Dec. 2013, pp. 1103–1108.

K. Wu, S. Yang, K.Q. Zhu, False rumours detection on Sina Weibo by propagation structures, in: Proceedings of the IEEE Thirty First International Conference on Data Engineering (ICDE), IEEE, 2015, pp. 651–662.

Chakraborty, B. Paranjape, S. Kakarla, N. Ganguly, Stop clickbait: “detecting and preventing clickbaits in online news media,” in: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2016, pp. 9–16.

D. Chaudhary and E. R. Vasuja, “A Review on Various Algorithms used in Machine Learning,” 2019

N. K. Chauhan and K. Singh, “A review on conventional machine learning vs deep learning,” in 2018 International Conference on Computing, Power and Communication Technologies (GUCON), 2018, pp. 347– 352.

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerg. Artif. Intell. Appl. Comput. Eng., vol. 160, pp. 3–24, 2007.

N. Nikolaou, H. Reeve, G. Brown, Margin Maximization as Lossless Maximal Compression, 2020, 200110318.

Y.C.P. Reddy, P. Viswanath, B.E. Reddy, Semi-supervised learning: a brief review, Int. J. Eng. Technol. 7 (1.8) (2018) 81.

Alkhodair et al, S. A., Fung, B. C. M., Ding, S. H. H., Cheung, W. K., & Huang, S.-C. (2021). “Detecting High-Engaging Breaking News Rumours in Social Media”. ACM Transactions on Management Information Systems.

Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). “Detection and Resolution of Rumours in Social Media”. ACM Computing Surveys.

Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016). “Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads”. PLOS ONE.

Zahra Zojaji, Behrouz Tork Ladani” Adaptive cost sensitive stance classification model for rumour detection in social networks”

Hadeer Sanaa Nagy Ramadan Hesham A. Hefny” Towards Rumours Detection Framework for social media” International Journal of Computer Applications (0975 – 8887) Volume 177 – No. 31, January 2020

Dr. Anil Kr. Dubey, Apoorv Singhal, Sarthak Gupta “Rumour Detection System using Machine Learning” International Research Journal of Engineering and Technology (IRJET).

Alkhodair, S. A., Ding, S. H. H., Fung, B. C. M., & Liu, J. (2019). “Detecting breaking news rumors of emerging topics in social media. Information Processing & Management”.

Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2020). The Future of False Information Detection on Social Media. ACM Computing Surveys,

[28] Victoria L. Rubin and Tatiana Lukoianova. 2015. Truth and deception at the rhetorical structure level. J. Assoc. Inf. Sci. Technol. 66, 5 (2015), 905–917.

ALDayel, A., & Magdy, W. (2021). Stance detection on social media: State of the art and trends. Information Processing & Management.

Sahoo, S. R., & Gupta, B. B. (2020). “Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing”

H. Ahmed, I. Traore, S. Saad, Detection of online fake news using ngram study and machine learning techniques, in: International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, Springer, Cham, 2017, pp. 127–138.

J. Zhang, L. Cui, Y. Fu, F.B. Gouza, Fake news detection with deep diffusive network model, 2018, arXiv preprint arXiv:1805.08751

Li Tan ,Ge Wang, Feiyang Jia , Xiaofeng Lian “Research status of deep learning methods for rumour detection” Multimedia Tools and Applications (2023)

Zhou X, Wu J, Zafarani R (2020) Safe: Similarity-aware multi-modal fake news detection. Advances in Knowledge discovery and data mining 12085:354

Tu K, Chen C, Hou C, Yuan J, Li J, Yuan X (2021) Rumor2vec: a rumor detection framework with joint text and propagation structure representation learning. Info Sci 560:137–151.

P, K., & Sridhar, R. (2020). Prediction of Social Influence for Provenance of Misinformation in Online Social Network Using Big Data Approach. The Computer Journal

Riloff, E., Qadir, A., Surve, P., De, L., Gilbert, N. and Huang, R. (2013) Sarcasm as Contrast Between a Positive Sentiment and Negative Situation. In Proc. 2013 Conf. Empirical Methods in Natural Language Processing.

Ying, Q.F., Chiu, D.M., Venkatramanan, S. and Zhang, X. (2018) User modelling and usage profiling based on temporal posting behaviour in OSNs.

Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physical A: Statistical Mechanics and Its Applications.

H. Zhu, H. Wu, J. Cao, G. Fu, H. Li, Information dissemination model for social media with constant updates, Physica A: Statistical Mechanics and its Applications, (2018)

K. Shu, S. Wang, H. Liu, Understanding user profiles on social media for fake news detection, In: Proceedings of the 1st IEEE International Workshop on Fake Multimedia, USA, 2018.

S. Tschiatschek, A. Singla, M. Gomez Rodriguez, A. Merchant, A. Krause, Fake news detection in social networks via crowd signals, In: Proceedings of the World Wide Web Conferences, France, 2018.

G. B. Guacho, S. Abdali, E. E. Papalexakis, Semi-supervised content-based fake news detection using tensor embeddings and label propagation, In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).




How to Cite

Mukiri , R. K. M. ., & Burra , V. B. . (2024). Rumour Source Identification Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 637 –. Retrieved from



Research Article