Abstract
Nowadays, social networks are widely used not only by humans, but also by bots (automated agents). Recent studies have reported that bot accounts are used across different dimensions and in different granularities (e.g. performing terrorist propaganda, spreading misinformation and polluting content). Bot detection has become a significant challenge, especially on online social networks. Today, researchers through Twitter are attempting to propose approaches for bot detection. However, they are confronted with certain challenges owing to the problems inherent to text and the use of language-dependent features. Therefore, we defined a set of statistical features which proved their importance in our work. Our proposed features are based on how much the others interact with the posts of the user, when the user interacts and how much the user interacts. We demonstrated that the mere use of information from the metadata of user profiles and the metadata of posts with a Deep Forest algorithm is sufficient in order to detect bot accounts accurately. In fact, this yielded an Accuracy result of 97.55%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Subrahmanian, V.S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Stevens, A.: The DARPA Twitter bot challenge. arXiv preprint arXiv:1601.05140 (2016)
Varol, O., Ferrara, E., Davis, C., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: 11th International AAAI Conference on Web and Social Media, pp. 1–10. AAAI, Canada (2017)
Kantepe, M., Ganiz, M.C.: Preprocessing framework for Twitter bot detection. In: Computer Science and Engineering (UBMK), pp. 630–634. IEEE, Turkey (2017)
Pozzana, I., Ferrara, E.: Measuring bot and human behavioral dynamics. arXiv preprint arXiv:1802.04286 (2018)
Chavoshi, N., Hamooni, H., Mueen, A.: On-demand bot detection and archival system. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 183–187. ACM, Australia (2017)
Cai, C., Li, L., Zengi, D.: Behavior enhanced deep bot detection in social media. In: International Conference on Intelligence and Security Informatics (ISI), pp. 128–130. IEEE, China (2017)
Ferrara, E.: Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday, 22(8) (2017)
Guimaraes, R.G., Rosa, R.L., De Gaetano, D., Rodriguez, D.Z., Bressan, G.: Age groups classification in social network using deep learning. IEEE Access 5, 10805–10816 (2017)
Kim, S.M., Paris, C., Power, R., Wan, S.: Distinguishing individuals from organisations on Twitter. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 805–806. ACM, Australia (2017)
Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)
Wu, T., Wen, S., Liu, S., Zhang, J., Xiang, Y., Alrubaian, M., Hassan, M.M.: Detecting spamming activities in twitter based on deep-learning technique. Concurr. Comput. : Pract. Exp. 29(19), e4209 (2017)
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling. IEEE Trans. Dependable Secure Comput. 15(4), 561–576 (2018)
Morstatter, F., Wu, L., Nazer, T.H., Carley, K.M., Liu, H.: A new approach to bot detection: striking the balance between precision and recall. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 533–540. IEEE Press, USA (2016)
Gilani, Z., Kochmar, E., Crowcroft, J.: Classification of twitter accounts into automated agents and human users. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 489–496. ACM, Australia (2017)
Bindu, P.V., Mishra, R., Thilagam, P.S.: Discovering spammer communities in Twitter. J. Intell. Inf. Syst., 1–25 (2018)
Tavares, G.M., Mastelini, S.M., Barbon Jr., S.: User classification on online social networks by post frequency. CEP 86057, 970 (2017)
Lee, K., Eoff, B.D., Caverlee, J.: Seven months with the devils: a long-term study of content polluters on Twitter. In: ICWSM, pp. 185–192 (2011)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. arXiv preprint arXiv:1702.08835 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Daouadi, K.E., Rebaï, R.Z., Amous, I. (2019). Bot Detection on Online Social Networks Using Deep Forest. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-19810-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19809-1
Online ISBN: 978-3-030-19810-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)