Social Bots Detection: A Discussion about Traditional Approaches Lifetime
Abstract
Social bot detection algorithms seem to aim a common thread: accuracy. However, if accuracy can be lost as bots evolve, the lifetime of these solutions must also be discussed. Using datasets from 2010 to 2018 to represent this evolution, three traditional models were evaluated to understand this lifetime. Looking at the performance changes, it was possible to see a loss of accuracy over the years and that it reflects a gradual change in the behavior of bots. Other factors such as heterogeneity of data and the ability to remain capable of detecting human accounts over the years (true negatives) are also discussed.
Keywords:
Social Bots, Machine Learning, Online Social Networks, Network Security
References
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Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, v. 20, n. 3, p. 273–297.
Cresci, S. (2020). "A decade of social bot detection". Communications of the ACM, v. 63, n. 10, p. 72–83.
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A. and Tesconi, M. (2015). "Fame for sale: Efficient detection of fake Twitter followers". Decision Support Systems, v. 80, p. 56–71.
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A. and Tesconi, M. (2017). "The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race".
Cresci, S., Petrocchi, M., Spognardi, A. and Tognazzi, S. (2019). "Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection".
Echeverría, J., De Cristofaro, E., Kourtellis, N., et al. (2018). "LOBO: Evaluation of Generalization Deficiencies in Twitter Bot Classifiers", Proceedings of the 34th Annual Computer Security Applications Conference. ACM.
Egli, A., Rosati, P., Lynn, T. and Sinclair, G. (2021). "Bad Robot: A Preliminary Exploration of the Prevalence of Automated Software Programmes and Social Bots"
Ferrara, E., Varol, O., Davis, C., Menczer, F. and Flammini, A. (2016). "The rise of social bots". Communications of the ACM, v. 59, n. 7, p. 96–104.
Jr, S. B., Campos, G. F. C., Tavares, G. M., et al. (2018). "Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets". ACM Transactions on Multimedia Computing, Communications, and Applications, v. 14, n. 1s, p. 1–17.
Kramer, O. (2013). K-Nearest Neighbors. In: Kramer, O.[Ed.]. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg. p. 13–23.
Latah, M. (2020). "Detection of malicious social bots: A survey and a refined taxonomy". Expert Systems with Applications, v. 151, p. 113383.
Popovic, D. (2000). "Intelligent Control with Neural Networks". Soft Computing and Intelligent Systems. Elsevier. p. 419–467.
Rauchfleisch, A. and Kaiser, J. (2020). "The False positive problem of automatic bot detection", social science research. PLOS ONE, v. 15, n. 10, p. e0241045.
Rodríguez-Ruiz, J., Mata-Sánchez, J. I., Monroy, R., Loyola-González, O. and López-Cuevas, A. (2020). "A one-class classification approach for bot detection on Twitter". Computers & Security, v. 91, p. 101715.
Samper-Escalante, L. D., Loyola-González, O., Monroy, R. and Medina-Pérez, M. A. (2021). "Bot Datasets on Twitter: Analysis and Challenges". Applied Sciences, v. 11, n. 9, p. 4105.
Santos, B., Ferreira G., do Ó, M, Braz, R. and Digiampietri, L. (2020). "Comparação de algoritmos para detecção de bots sociais nas eleições presidenciais no Brasil em 2018 utilizando características do usuário". Revista Brasileira de Computação Aplicada, v. 13, n. 1.
Santos, J., Ituassu, A., Lifschitz, S., et al. (2021). "Das milícias digitais ao comportamento coordenado: métodos interdisciplinares de análise e identificação de bots nas eleições brasileiras". 2021: ANAIS DO X BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING, p. 187–192.
Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A. and Menczer, F. (2020). "Detection of Novel Social Bots by Ensembles of Specialized Classifiers".
Stein, T., Chen, E. and Mangla, K. (2011). "Facebook immune system", Proceedings of the 4th Workshop on Social Network Systems - SNS ’11. ACM
Wang, P., Angarita, R. and Renna, I. (2018). "Is this the Era of Misinformation yet: Combining Social Bots and Fake News to Deceive the Masses", Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. ACM Press.
Yang, K.-C., Varol, O., Hui, P.-M. and Menczer, F. (2020). "Scalable and Generalizable Social Bot Detection through Data Selection", Proceedings of the AAAI Conference on Artificial Intelligence, v. 34, n. 01, p. 1096–1103.
Published
2021-10-04
How to Cite
DA MATA, Erick N.; DIAS, Gabriela M. S.; SALLES, Ronaldo M..
Social Bots Detection: A Discussion about Traditional Approaches Lifetime. In: BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 21. , 2021, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 337-350.
DOI: https://doi.org/10.5753/sbseg.2021.17326.
