Skip to main content

Rumour veracity detection on twitter using particle swarm optimized shallow classifiers


Information overload on Web has been a well-identified challenge which has amplified with the advent of social web. Good, bad, true, false, useful, useless all kinds of information disseminates through the social web platforms. It becomes exceedingly imperative to pro-actively resolve rumours and inhibit them from spreading among the Internet users as it can jeopardize the well-being of the citizens. The task for rumour analysis intends to identify & classify a rumour either as true (factual), false (nonfactual) or unresolved. Determining the accuracy of a rumourous story, a.k.a. rumour veracity is hard owing to the noisy, ambiguous and heterogeneous use of natural language. This necessitates automation of the predictive task which classifies the questionable veracity of rumour accurately. The research presented in this paper, is an empirical study to put forward an optimized learning model which classifies real-time tweets on the basis of truth value, facilitating rumour analysis. The study is conducted on a collection of nearly 14 k tweets pertaining to the recent mob lynching fuelled by rumours on suspected child-lifters in the Indian sub-continent (#moblynching) and run on five classical shallow classifiers to categorize tweets into true, false and unspecified using 13 attributes (features). Subsequently, the use of an optimal feature selection method, particle swarm algorithm is proposed to improve the classifier’s performance. The empirical analysis validates that the proposed implementation of particle swarm optimization (PSO) for feature subset selection in rumour veracity classification outperforms the baseline supervised learning algorithms. An average 11.28% improvement in accuracy and approximately 31% average reduction in features are demonstrated using PSO. The highest accuracy with optimization of 96.15% is achieved by decision tree.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. Aggarwal A, Kumar S, Bhargava K, Kumaraguru P (2018) The follower count fallacy: detecting twitter users with manipulated follower count. Proceedings of SAC 2018: symposium on applied Computing 8

  2. Aghdam MH, Heidari S (2015) Feature selection using particle swarm optimization in text categorization. J Artif Intell Soft Comput Res 5(4):231–238

    Article  Google Scholar 

  3. Alzubi J, Nayyar A, Kumar A (2018) Machine learning from theory to algorithms: an overview. J Phys: Conf Ser 1142(1):012012

    Google Scholar 

  4. Boididou C, Middleton SE, Jin Z, Papadopoulos S, Dang-Nguyen D-T, Boato G, Kompatsiaris Y (2018) Verifying information with multimedia content on twitter. Multimed Tools Appl 77(12):15545–15571.

    Article  Google Scholar 

  5. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter, Proceedings of the 20th International Conference on World Wide Web. ACM: 675–684

  6. Chang C, Zhang Y, Szabo C, Sheng QZ (2016) Extreme user and political rumor detection on twitter. Proceedings of 12th International Conference Advanced Data Mining and Applications, Springer, 751–763

  7. Chua Alton YK, Banerjee S (2016) Linguistic predictors of rumor veracity on the internet. Proceedings of the international multi conference of engineers and computer scientists 1

  8. Enayet O, El-Beltagy SR (2017) NileTMRG at SemEval-2017 task 8: determining rumour and veracity support for rumours on twitter. Proceedings of SemEval. ACL

  9. Giasemidis G, Singleton C, Agrafiotis I, Jason RC, Pilgrim A, Willis C, Greetham DV (2016) Determining the veracity of rumours on twitter. International conference on social informatics. Springer: 185–205

  10. Gorrell G, Bontcheva K, Derczynski L, Kochkina E, Liakata M, Zubiaga A, Eval R (2019) Determining rumour veracity and support for rumours

  11. Indian Express Website (2018)]

  12. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw Perth: 1942–1948

  13. Kennedy J, Eberhart RC (1995) A new optimizer using particle swarm theory Sixth International Symposium on Micro Machine and Human Science, Nagoya: 39–43

  14. Kumar A, Jaiswal A (2017) Empirical study of twitter and Tumblr for sentiment analysis using soft computing techniques. Int Conf Soft Comput Applic (ICSCA 2017), World Congress Eng Comput Sci 1:1–5

    Google Scholar 

  15. Kumar A, Jaiswal A (2019) Systematic literature review of sentiment analysis on twitter using soft computing techniques. Concurrency Computat Pract Exper: e5107. doi:

  16. Kumar A, Sangwan SR (2018) Information Virality prediction using emotion quotient of tweets. Int J Comput Sci Eng 6(6):642–651

    Google Scholar 

  17. Kumar A, Sangwan SR (2018) Rumour detection using machine learning techniques on social media, International Conference on Innovative Computing and Communication. Lecture Notes in Networks and Systems, Springer

  18. Kumar A, Dogra P, Dabas V (2015) Emotion analysis of twitter using opinion mining international conference on contemporary computing (IC3). IEEE, 285–290

  19. Kumar A, Khorwal R, Chaudhary S (2016) A survey on sentiment analysis using swarm intelligence, Indian J Sci Technol 9(39)

  20. Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. 13th international conference on data mining. IEEE: 1103–1108

  21. Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS One 12:1

    Google Scholar 

  22. Liu X, Nourbakhsh A, Li Q, Fang R, Shah S (2015) Real-time rumor debunking on Twitter. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM: 1867–1870

  23. Loper E, Bird S (2002) 2002. NLTK: the natural language toolkit, proceedings of the ACL-02 workshop on effective tools and methodologies for teaching natural language processing and computational linguistics. Assoc Comput Linguist 1:63–70

    Google Scholar 

  24. Ma J, Gao W, Wei Z, Lu Y, Wong K (2015) Detect rumors using time series of social context information on microblogging websites. Proceedings of the 24th ACM international on conference on Information and Knowledge Management ACM: 1751–1754

  25. Ma B, Lin D, Cao D (2017) Content representation for microblog rumor detection. Advances in Computational Intelligence Systems Springer: 245–251

  26. Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In Proc ESWC-11

  27. Omar N, Jusoh F, Ibrahim R et al (2013) Review of feature selection for solving classification problems. J Inform Syst Res Innov 3:64–70

    Google Scholar 

  28. Porter MF (1980) An algorithm for suffix stripping. Program 14(3):130–137 Available :

    Article  Google Scholar 

  29. Serrano E, Iglesias CA, Garijo M (2015) A survey of twitter rumour spreading simulations, computational collective intelligence. Lecture notes in computer science, Vol 9329. Springer, pp 113–122

  30. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. Proc IEEE Int Conf Evolutionary Computation. Anchorage, AK, USA: 69–73

  31. Veyseh A Ebrahimi J, Dou D, Lowd D (2017) A Temporal Attentional Model for Rumor Stance Classification. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17). ACM, 2335–2338

  32. Vosoughi S (2015) Automatic detection and verification of rumors on twitter. Ph.D. Dissertation

  33. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):11461151

    Article  Google Scholar 

  34. Wang S, Terano T (2015) Detecting rumor patterns in streaming social media. Proceedings of the 2015 IEEE international conference on big data (big Data’15). IEEE: 2709–2715

  35. Wang X, Yang J, Teng X (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Article  Google Scholar 

  36. Wu K, Yang S, Zhu KQ (2015) False rumors detection on sinaweibo by propagation structures. Proceedings of the 2015 IEEE 31st international conference on data engineering. IEEE: 651–662

  37. Yang F, Liu L, Yu X, Yang M (2012) Automatic detection of rumor on Sina Weibo. Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics ACM: 13

  38. Zhang Z, Zhang Z, Li H (2015) Predictors of the authenticity of internet health rumours. Health Inform Libr J 32(3):195–205

    Article  Google Scholar 

  39. 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):32

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Anand Nayyar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Sangwan, S.R. & Nayyar, A. Rumour veracity detection on twitter using particle swarm optimized shallow classifiers. Multimed Tools Appl 78, 24083–24101 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Rumour
  • Classifier
  • Veracity
  • Feature selection
  • Swarm