D-BullyRumbler: a safety rumble strip to resolve online denigration bullying using a hybrid filter-wrapper approach


Denigration is a specialized form of cyberbullying which describes a recurrent, sustained and intentional attempt to damage the victim’s reputation or ruin the friendships that he or she has by spreading unfounded gossip or rumors online. It is the most common bullying tactic involving character assassination of public figures like celebrities and politicians. As a comprehensive approach to match to the scale of social media this research put forwards a D-BullyRumbler model for automatic detection and resolution of denigration cyberbullying in online textual content using a hybrid of lexicon-based and machine learning-based techniques. The model processes textual, content-based and user-based features to uncover denigration from two perspectives. Firstly, a direct explicit content analysis is done to look for denigration markers as features for model training and testing. Concurrently, potentially harmful messages, rumors, are identified as candidates and examined for target profile type to reveal the case of denigration. An additional OR operation is done to maintain the holistic framework. Another novelty of the work includes the use of hybrid filter-wrapper method, Chi-square filter and cuckoo search wrapper algorithm to improve the performance of reputation rumor classification module. Experimental results on social media datasets show the superior classification performance. The results validate the effectiveness of the proposed model which facilitates timely intervention by buzzing an alarm to the moderators and further forming a rumble safety strip to inhibit the production and dissemination of inappropriate content to protect the victims.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.


  2. 2.


  3. 3.

    SMS Dictionary. Vodacom Messaging. Retrieved 16 March 2012.

  4. 4.


  5. 5.


  6. 6.



  1. 1.

    Dwivedi, Y.K., Kelly, G., Janssen, M., Rana, N.P., Slade, E.L., Clement, M.: Social media: the good, the bad, and the ugly. Inf. Syst. Front. 20(3), 419–423 (2018)

    Article  Google Scholar 

  2. 2.

    Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78, 23973–24010 (2019)

    Article  Google Scholar 

  3. 3.

    Smith, P. K., Mahdavi, J., Carvalho, M., Tippett, N.: An investigation into cyberbullying, its forms, awareness and impact, and the relationship between age and gender in cyberbullying. Research Brief No. RBX03-06. DfES, London (2006)

  4. 4.

    Sathyanarayana Rao, T.S., Bansal, D., Chandran, S.: Cyberbullying: a virtual offense with real consequences. Indian J Psychiatr. 60(1), 3–5 (2018). https://doi.org/10.4103/psychiatry.IndianJPsychiatry_147_18

    Article  Google Scholar 

  5. 5.

    Marwick, A., Miller, R.: Online Harassment, Defamation, and Hateful Speech: A Primer of the Legal landscape. Fordham Center on Law and Information Policy, New York (2014)

    Google Scholar 

  6. 6.

    Lindsey, G.: Traffic impacts of bicycle facilities. Minnesota Department of Transportation-Research Project Final Report 2017-23 (2017)

  7. 7.

    Omar, N., Jusoh, F., Ibrahim, R., Othman, M.S.: Review of feature selection for solving classification problems. J. Inf. Syst. Res. Innov. 3, 64–70 (2013)

    Google Scholar 

  8. 8.

    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214. IEEE (2009)

  9. 9.

    Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 2, pp. 241–244. IEEE (2011)

  10. 10.

    Dinakar, K., Jones, B., Havasi, C., Lieberman, H., Picard, R.: Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans. Interact. Intell. Syst (TiiS) 2(3), 18 (2012)

    Google Scholar 

  11. 11.

    Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. Proc. Content Anal. WEB 2, 1–7 (2009)

    Google Scholar 

  12. 12.

    Nahar, V., Al-Maskari, S., Li, X., Pang, C.: Semi-supervised learning for cyberbullying detection in social networks. In: Australasian Database Conference, pp. 160–171. Springer, Cham (2014)

    Google Scholar 

  13. 13.

    Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., Hoste, V.: Automatic detection and prevention of cyberbullying. In: International Conference on Human and Social Analytics (HUSO 2015), pp. 13–18. IARIA (2015)

  14. 14.

    Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., Hoste, V.: Detection and fine-grained classification of cyberbullying events. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 672–680 (2015)

  15. 15.

    Al-garadi, M.A., Varathan, K.D., Ravana, S.D.: Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput. Human Behav. 63, 433–443 (2016)

    Article  Google Scholar 

  16. 16.

    Xu, J.M., Jun, K.S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 656–666. Association for Computational Linguistics (2012)

  17. 17.

    Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, p. 43. ACM (2016)

  18. 18.

    Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. European Conference on Information Retrieval, pp. 693–696. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  19. 19.

    Dadvar, M., Jong, F.D., Ordelman, R., Trieschnigg, D.: Improved cyberbullying detection using gender information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012). University of Ghent (2012)

  20. 20.

    Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 409–416. ACM (2017)

  21. 21.

    Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput. Security 90, 101710 (2020)

    Article  Google Scholar 

  22. 22.

    Kumar, A., Sachdeva, N.: Cyberbullying checker: online bully content detection using hybrid supervised learning. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 371–382. Springer, Singapore (2020)

    Google Scholar 

  23. 23.

    Dadvar, M., Eckert, K.: Cyberbullying detection in social networks using deep learning based models; a reproducibility study. (2018). arXiv:1812.08046

  24. 24.

    Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. In: European Conference on Information Retrieval, pp. 141–153. Springer, Cham (2018)

    Google Scholar 

  25. 25.

    Cheng, L., Guo, R., Silva, Y., Hall, D., Liu, H.: Hierarchical attention networks for cyberbullying detection on the instagram social network. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 235–243. Society for Industrial and Applied Mathematics (2019)

  26. 26.

    Al-Hashedi, M., Soon, L.K., Goh, H.N.: Cyberbullying detection using deep learning and word embeddings: an empirical study. In: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems, pp. 17–21 (2019)

  27. 27.

    Founta, A.M., Chatzakou, D., Kourtellis, N., Blackburn, J., Vakali, A., Leontiadis, I.: A unified deep learning architecture for abuse detection. In: Proceedings of the 10th ACM Conference on Web Science, pp. 105–114 (2019)

  28. 28.

    Mahlangu, T., Tu, C.: Deep learning cyberbullying detection using stacked embbedings approach. In: 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 45–49. IEEE (2019)

  29. 29.

    Singh, V.K., Ghosh, S., Jose, C.: Toward multimodal cyberbullying detection. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2090–2099 (2017)

  30. 30.

    Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., Predovic, G.: Exploring deep multimodal fusion of text and photo for hate speech classification. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 11–18 (2019)

  31. 31.

    Kansara, K.B., Shekokar, N.M.: A framework for cyberbullying detection in social network. Int J Current Eng Technol 5(1), 494–498 (2015)

    Google Scholar 

  32. 32.

    He, Z.: Exploiting the topology property of social network for rumor detection. In: 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 41–46. IEEE (2015)

  33. 33.

    Kumar, A., Sangwan, S.R., Nayyar, A.: Rumour veracity detection on twitter using particle swarm optimized shallow classifiers. Multimed. Tools Appl. 78(17), 24083–24101 (2019)

    Article  Google Scholar 

  34. 34.

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

  35. 35.

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

  36. 36.

    Zhang, Q., Zhang, S., Dong, J., Xiong, J., Cheng, X.: Automatic detection of rumor on social network. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science, vol 9362, pp. 113–122. Springer, Cham (2015)

    Google Scholar 

  37. 37.

    Jin, Z., Cao, J., Jiang, Y. G., Zhang, Y.: News credibility evaluation on microblog with a hierarchical propagation model. In: 2014 IEEE International Conference on Data Mining, pp. 230–239. IEEE (2014)

  38. 38.

    Wang, S., Terano, T.: Detecting rumor patterns in streaming social media. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2709–2715. IEEE (2015)

  39. 39.

    Sahana, V.P., Pias, A.R., Shastri, R., Mandloi, S.: Automatic detection of rumoured tweets and finding its origin. In: 2015 International Conference on Computing and Network Communications (CoCoNet), pp. 607–612. IEEE (2015)

  40. 40.

    Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1405. International World Wide Web Conferences Steering Committee (2015). https://doi.org/10.1007/978-3-319-67217-5_8

  41. 41.

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

  42. 42.

    Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1103–1108. IEEE (2013)

  43. 43.

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

    Google Scholar 

  44. 44.

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

  45. 45.

    Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J.R., Pilgrim, A., Willis, C., Greetham, D.V.: Determining the veracity of rumours on Twitter. In: International Conference on Social Informatics, pp. 185–205. Springer, Cham (2016)

    Google Scholar 

  46. 46.

    Kwon, S., Cha, M., Jung, K.: Rumor detection over varying time windows. PLoS One 12(1), e0168344 (2017)

    Article  Google Scholar 

  47. 47.

    Jin, Z., Cao, J., Zhang, Y., Zhou, J., Tian, Q.: Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimed. 19(3), 598–608 (2017)

    Article  Google Scholar 

  48. 48.

    Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 40–52. Springer, Cham (2018)

    Google Scholar 

  49. 49.

    Jin, Z., Cao, J., Guo, H., Zhang, Y., Luo, J.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 795–816. ACM (2017)

  50. 50.

    Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A convolutional approach for misinformation identification. In: IJCAI, pp. 3901–3907 (2017)

  51. 51.

    Kumar, A., Singh, V., Ali, T., Pal, S., Singh, J.: Empirical evaluation of shallow and deep classifiers for rumor detection. In: Sharma, H., Govindan, K., Poonia, R.C., Kumar, S., El-Medany, W.M. (eds.) Advances in Computing and Intelligent Systems, pp. 239–252. Springer, Singapore (2020)

    Google Scholar 

  52. 52.

    Bhatia, M.P.S., Sangwan, S.R.: Debunking online reputation rumours using hybrid of lexicon-based and machine learning techniques. In: Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), pp. 317–327. Springer, Singapore (2020)

    Google Scholar 

  53. 53.

    Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. LREC 6, 417–422 (2006)

    Google Scholar 

  54. 54.

    Zanaty, E.A.: Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egypt. Inf. J. 13(3), 177–183 (2012)

    Google Scholar 

  55. 55.

    Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. ACM Comput. Surveys (CSUR) 51(2), 1–36 (2018)

    Article  Google Scholar 

  56. 56.

    Bhatia, M.P.S., Kumar, A.: A primer on the web information retrieval paradigm. J. Theor. Appl. Inf. Technol. 4(7) (2008)

  57. 57.

    Jain, D.K., Kumar, A., Sangwan, S.R., Nguyen, G.N., Tiwari, P.: A particle swarm optimized learning model of fault classification in Web-Apps. IEEE Access 7, 18480–18489 (2019)

    Article  Google Scholar 

  58. 58.

    Hsu, Hui-Huang, Hsieh, Cheng-Wei, Lu, Ming-Da: Hybrid feature selection by combining filters and wrappers. Expert Syst. Appl. 38, 8144–8150 (2011). https://doi.org/10.1016/j.eswa.2010.12.156

    Article  Google Scholar 

  59. 59.

    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: European Conference on Machine Learning, pp. 137–142. Springer, Berlin, Heidelberg (1998)

    Google Scholar 

  60. 60.

    Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter. Multimed. Tools Appl. 78, 29529 (2019)

    Article  Google Scholar 

  61. 61.

    Krafft, P.M., Spiro, E.S.: Keeping rumors in proportion: managing uncertainty in rumor systems. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, p. 646. ACM (2019)

  62. 62.

    Kumar, A., Srinivasan, K., Cheng, W.H., Zomaya, A.Y.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)

    Article  Google Scholar 

  63. 63.

    Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter R.: PHEME dataset of rumours and non-rumours (2016)

  64. 64.

    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Gonzlez, J., Pelta, D., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Vol. 284 of Studies in Computational Intelligence, pp. 65–74. Springer, Berlin, Heidelberg (2010)

    Google Scholar 

  65. 65.

    Dorigo, M.: Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano (1992)

Download references

Author information



Corresponding author

Correspondence to Saurabh Raj Sangwan.

Ethics declarations

Conflict of interest

The authors certify that there is no conflict of interest in the subject matter discussed in this manuscript.

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

Sangwan, S.R., Bhatia, M.P.S. D-BullyRumbler: a safety rumble strip to resolve online denigration bullying using a hybrid filter-wrapper approach. Multimedia Systems (2020). https://doi.org/10.1007/s00530-020-00661-w

Download citation


  • Cyberbullying
  • Denigration
  • Machine learning
  • Filter-wrapper
  • Rumor
  • Social media