A Holistic Approach for Detecting Socialbots on Twitter: Integration of Diverse Features


  • M. Owais Computer Science, University of Engineering & Technology 54890, Punjab, Pakistan
  • M. Shoaib Computer Science, University of Engineering & Technology 54890, Punjab, Pakistan
  • M. Waseem Computer Science, University of Engineering & Technology 54890, Punjab, Pakistan


The usage of social media platforms has grown, offering individuals diverse avenues for communication, expressing opinions and sharing online content. However, this surge has also given rise to the emergence of social bots, which are programmed accounts designed to imitate human behavior. Such bots possess the capability to disseminate false information, manipulate financial markets, aid terrorism, and disrupt democratic processes. To tackle this issue, various approaches have been utilized to detect social bots, including approaches based on profiles, time patterns, content analysis, behavior, and network characteristics. However, neither of the approaches effectively combines all these features to implement social bot detection comprehensively. This paper introduces an ensemble methodology that merges profile, behavioral, temporal, network, graph, and content-based attributes, culminating in a comprehensive model for discerning social bots on the Twitter platform. We utilize the Twibot-22 dataset for conducting experiments and evaluate the performance of our approach against benchmark models. The XGBoost model, with an accuracy of 0.898, exhibited superior performance compared to the benchmark models. This research contributes to the continuous endeavor focused on safeguarding the authenticity of tweet content and mitigating the risks associated with social bots on social networks.


K. Hayawi, S. Mathew, N. Venugopal, M.M. Masud, and P.H. Ho, "DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data," Social Network Analysis and Mining, vol. 12, pp. 43, 2022.

J. León-Quismondo, "Social Sensing and Individual Brands in Sports: Lessons Learned from English-Language Reactions on Twitter to Pau Gasol’s Retirement Announcement," International Journal of Environmental Research and Public Health, pp. 895, 2023.

R. Gilmary, A. Venkatesan, and G. Vaiyapuri, "Detection of automated behavior on Twitter through approximate entropy and sample entropy," Personal and Ubiquitous Computing, pp. 1-15, 2021.

B. Wu, L. Liu, Y. Yang, K. Zheng, and X. Wang, "Using improved conditional generative adversarial networks to detect social bots on Twitter," IEEE Access, pp. 36664-36680, 2020.

M. Heidari, S. Zad, P. Hajibabaee, M. Malekzadeh, S. HekmatiAthar, O. Uzuner, and J.H. Jones, "Bert model for fake news detection based on social bot activities in the COVID-19 pandemic," IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2021.

M. Kolomeets, O. Tushkanova, D. Levshun, and A. Chechulin, "Camouflaged bot detection using the friend list," In 2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2021.

E. Puertas, L.G. Moreno-Sandoval, F.M. Plaza-del Arco, J.A. Alvarado-Valencia, A. Pomares-Quimbaya and L. Alfonso, "Bots and gender profiling on Twitter using sociolinguistic features," CLEF (Working Notes), pp. 1-8, 2019.

L. Ilias and I. Roussaki, "Detecting malicious activity in Twitter using deep learning techniques," Applied Soft Computing, pp. 107360, 2021.

R. Bailurkar and N. Raul, "Detecting bots to distinguish hate speech on social media," in International Conference on Computing Communication and Networking Technologies, 2021.

L. Alkulaib, L. Zhang, Y. Sun and C.T. Lu, "Twitter Bot Identification: An Anomaly Detection Approach," IEEE International Conference on Big Data (Big Data), 2022.

M. Mendoza, M. Tesconi and S. Cresci, "Bots in social and interaction networks: detection and impact estimation," ACM Transactions on Information Systems (TOIS), vol. 39, pp. 1-32, 2020.

S. Najari, M. Salehi and R. Farahbakhsh, "GANBOT: a GAN-based framework for social bot detection," Social Network Analysis and Mining, vol. 12, pp. 1-11, 2022.

M.M. Pingili, M.J. Lakshmi, M. Divya, D. Abhishek, K. Naresh and P. Sparsha, "Detection of malicious social bots using learning automata with URL features in twitter network," IEEE Transactions on Computational Social Systems, vol. 7(4), pp. 1004-1018, 2020.

Y. Wu, Y. Fang, S. Shang, J. Jin, L. Wei and H. Wang, "A novel framework for detecting social bots with deep neural networks and active learning," Knowledge-Based Systems, vol. 211, pp. 106525, 2021.

M. Mazza, S. Cresci, M. Avvenuti, W. Quattrociocchi and M. Tesconi, "Rtbust: Exploiting temporal patterns for botnet detection on twitter," Proceedings of the 10th ACM conference on web science, 2019.

E. Alothali, K. Hayawi and H. Alashwal, "Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter," Social Network Analysis and Mining, vol. 11, pp. 1-15, 2021.

H. Shukla, N. Jagtap and B. Patil, "Enhanced Twitter bot detection using ensemble machine learning," 6th International Conference on Inventive Computation Technologies (ICICT), 2021.

G. Lingam, R.R Rout, D.V. Somayajulu and S.K. Das, "Social botnet community detection: a novel approach based on behavioral similarity in twitter network using deep learning," Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, 2020.

S. Kumar, S. Garg, Y. Vats and A.S. Parihar, "Content based bot detection using bot language model and bert embeddings," 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021.

C. Monica and N. Nagarathna, "Detection of fake tweets using sentiment analysis," SN Computer Science, vol. 1, pp. 1-7, 2020.

D. Kosmajac and V. Keselj, "Twitter bot detection using diversity measures," Proceedings of the 3rd International Conference on Natural Language and Speech Processing, 2019.

N. Vo, K. Lee, C. Cao, T. Tran and H. Choi, "Revealing and detecting malicious retweeter groups," in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017.

S. Feng, Z. Tan, H. Wan, N. Wang, Z. Chen, B. Zhang, Q. Zheng, W. Zhang, Z. Lei, S. Yang and X. Feng, "TwiBot-22: Towards graph-based Twitter bot detection," 2022.

M.A. Hall, "Correlation-based feature selection for machine learning," The University of Waikato, 1999.




How to Cite

M. Owais, M. Shoaib, and M. Waseem, “A Holistic Approach for Detecting Socialbots on Twitter: Integration of Diverse Features”, The Nucleus, vol. 60, no. 2, pp. 199–204, Sep. 2023.