Using Neural Network for Identifying Clickbaits in Online News Media

  • Amin OmidvarEmail author
  • Hui Jiang
  • Aijun An
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)


Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017’s dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.


Clickbait detection Text classification Deep learning 


  1. 1.
    Anand, A., Chakraborty, T., Park, N.: We used neural networks to detect clickbaits: you won’t believe what happened next! In: Jose, J., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 541–547. Springer, Cham (2017). Scholar
  2. 2.
    Potthast, M., Gollub, T., Hagen, M., Stein, B.: The clickbait challenge 2017: towards a regression model for clickbait strength. In: Proceedings of the Clickbait Challenge (2017)Google Scholar
  3. 3.
    Potthast, M., et al.: Crowdsourcing a large corpus of clickbait on Twitter (2017, to appear)Google Scholar
  4. 4.
    Palau-Sampio, D.: Reference press metamorphosis in the digital context: clickbait and tabloid strategies in Elpais. com. vol. 29 (2016)Google Scholar
  5. 5.
    Rony, M., Hassan, N., Yousuf, M.: Diving deep into clickbaits: who use them to what extents in which topics with what effects? In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 232–239. ACM (2017)Google Scholar
  6. 6.
    Chakraborty, A., Sarkar, R., Mrigen, A., Ganguly, N.: Tabloids in the era of social media? Understanding the production and consumption of clickbaits in Twitter (2017)Google Scholar
  7. 7.
    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
  8. 8.
    Chakraborty, A., Paranjape, B., Kakarla, S., Ganguly, N.: Stop clickbait: detecting and preventing clickbaits in online news media. In: IEEE/ACM International Conference Advances in Social Networks Analysis and Mining (ASONAM), pp. 9–16. IEEE (2016)Google Scholar
  9. 9.
    Volkova, S., Shaffer, K., Jang, J., Hodas, N.: Separating facts from fiction: linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 647–653 (2017)Google Scholar
  10. 10.
    Zhou, Y.: Clickbait detection in tweets using self-attentive network. arXiv preprint arXiv:1710.05364 (2017)
  11. 11.
    Grigorev, A.: Identifying clickbait posts on social media with an ensemble of linear models. arXiv preprint arXiv:1710.00399 (2017)
  12. 12.
    Glenski, M., Ayton, E., Arendt, D., Volkova, S.: Fishing for clickbaits in social images and texts with linguistically-infused neural network models. arXiv preprint arXiv:1710.06390 (2017)
  13. 13.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  14. 14.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  15. 15.
    Thomas, P.: Clickbait identification using neural networks. arXiv preprint arXiv:1710.08721 (2017)
  16. 16.
    Cao, X., Le, T., et al.: Machine learning based detection of clickbait posts in social media. arXiv preprint arXiv:1710.01977 (2017)
  17. 17.
    Gairola, S., Lal, Y., Kumar, V., Khattar, D.: A neural clickbait detection engine. arXiv preprint arXiv:1710.01507 (2017)
  18. 18.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  19. 19.
    Taghipour, K., Ng, H.: A neural approach to automated essay scoring. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1882–1891 (2016)Google Scholar
  20. 20.
    Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820 (2015)
  21. 21.
    Elman, J.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)CrossRefGoogle Scholar
  22. 22.
    Potthast, M., Gollub, T., Rangel, F., Rosso, P., Stamatatos, E., Stein, B.: Improving the reproducibility of PAN’s shared tasks: In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 268–299. Springer, Cham (2014). Scholar
  23. 23.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceYork UniversityTorontoCanada

Personalised recommendations