Clickbait Detection Using Swarm Intelligence

  • Deepanshu Pandey
  • Garimendra Verma
  • Sushama Nagpal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


Clickbaits are the articles containing catchy headlines which lure the reader to explore full content, but do not have any useful information. Detecting clickbaits solely by the headline without opening the link, can serve as a utility for users over internet. This can prevent their time from useless surfing caused by exploring clickbaits. In this paper Ant Colony Optimization, a Swarm Intelligence (SI) based technique has been used to detect clickbaits. In comparison with algorithms used in the past, this SI based technique provided a better accuracy and a human interpretable set of rules to classify clickbaits. A maximum accuracy of 96.93% with a set of 20 classification rules was obtained using the algorithm.


Clickbaits Ant Colony Optimization (ACO) Classification 


  1. 1.
    Smith, S.F.: A Learning System Based on Genetic Adaptive Algorithms, pp. 1–214 (1980)Google Scholar
  2. 2.
    Smith, S.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings 8th International Joint Conference on Artificial Intelligence, pp. 422–425 (1983)Google Scholar
  3. 3.
    Chakraborty, A., Paranjape, B., Kakarla, S., Ganguly, N.: Stop clickbait: detecting and preventing clickbaits in online news media. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 9–16 (2016)Google Scholar
  4. 4.
    Taweesiriwate, A., Manaskasemsak, B., Rungsawang, A.: Web spam detection using link-based ant colony optimization. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, pp. 868–873 (2012)Google Scholar
  5. 5.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Trans. Evol. Comput. 17, 64–76 (2013)CrossRefGoogle Scholar
  6. 6.
    Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., Mcclosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)Google Scholar
  7. 7.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL 2005, pp. 363–370 (2005)Google Scholar
  8. 8.
    Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP (2013)Google Scholar
  9. 9.
    Downworthy: A browser plugin to turn hyperbolic viral headlines into what they really mean. Accessed 10 June 2018
  10. 10.
    Myra: A collection of ant colony optimization (ACO) algorithms for the data mining classification and regression tasks. Accessed 10 June 2018
  11. 11.
    Lowenstein, G.: The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116, 75 (1994)CrossRefGoogle Scholar
  12. 12.
    Becchetti, L., Castillo, C., Donato, D., Leonardi, S., Baeza-Yates, R.: Link-based characterization and detection of web spam. In: Proceedings of the 2nd International Workshop on Adversarial Information Retrieval on the Web (2006)Google Scholar
  13. 13.
    Ahmed, S., Monzur, R., Palit, R.: Development of a Rumor and spam reporting and removal tool for social media. In: 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (2016)Google Scholar
  14. 14.
    Potthast, M., Köpsel, S., Stein, B., Hagen, M.: Clickbait detection. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 810–817. Springer, Cham (2016). Scholar
  15. 15.
    Agrawal, A.: Clickbait detection using deep learning. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 268–272 (2016)Google Scholar
  16. 16.
    Anand, A., Chakraborty, T., Park, N.: We used neural networks to detect clickbaits: you won’t believe what happened next! In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 541–547. Springer, Cham (2017). Scholar
  17. 17.
    Clickbait Challenge 2017: Challenge on detection of clickbait posts in social media. Accessed 10 June 2018
  18. 18.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008). Scholar
  19. 19.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: Handling continuous attributes in ant colony classification algorithms. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp. 225–231 (2009)Google Scholar
  20. 20.
    Liu, B., Abbas, H., Mckay, B.: Classification rule discovery with ant colony optimization. In: IEEE/WIC International Conference on Intelligent Agent Technology, IAT 2003, pp. 83–88 (2003)Google Scholar
  21. 21.
    Chan, A., Freitas, A.: A new classification-rule pruning procedure for an ant colony algorithm. In: Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871, pp. 25–36. Springer, Heidelberg (2006). Scholar
  22. 22.
    El-Arini, K., Tang, J.: Click-Baiting: Facebook Newsroom. Accessed 10 June 2018
  23. 23.
    Parpinelli, R.S., Lopes, H.S., Frietas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6, 321–332 (2002)CrossRefGoogle Scholar
  24. 24.
    Tian, J., Yu, W., Xie, S.: An ant colony optimization algorithm for image edge detection. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 751–756 (2008)Google Scholar
  25. 25.
    Cao, X., Le, T., Zhang, J., Lee, D.: Machine learning based detection of clickbait posts in social media. In: Submission for the Clickbait Challenge. arXiv preprint arXiv:1710.01977 (2017)
  26. 26.
    Baesens, B., Setiono, R., Mues, C., Vanthienen, J.: Using neural network rule extraction and decision tables for credit-risk evaluation. Manage. Sci. 49, 312–329 (2003)CrossRefGoogle Scholar
  27. 27.
    Mani, S., Shankle, W.R., Pazzani, M.J.: Acceptance of rules generated by machine learning among medical experts. Methods Inf. Med. 40, 380–385 (2001)CrossRefGoogle Scholar
  28. 28.
    Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11, 651–665 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Deepanshu Pandey
    • 1
  • Garimendra Verma
    • 1
  • Sushama Nagpal
    • 1
  1. 1.Netaji Subhas Institute of TechnologyDelhiIndia

Personalised recommendations