A systematic analysis of random forest based social media spam classification

Al-Janabi, Mohammed; Andras, Peter

Abstract

Recently random forest classification became a popular choice machine learning applications aimed to detect spam content in online social networks. In this paper, we report a systematic analysis of random forest classification for this purpose. We assessed the impact of key parameters, such as number of trees, depth of trees and minimum size of leaf nodes on classification performance. Our results show that controlling the complexity of random forest classifiers applied to social media spam is important in order to avoid overfitting and optimize performance We also conclude that in order to support reproducibility of experimental results it is important to report key parameters of random forest classifiers.

Citation

Al-Janabi, M., & Andras, P. (2017, August). A systematic analysis of random forest based social media spam classification. Presented at NSS: International Conference on Network and System Security, Helsinki, Finland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: NSS: International Conference on Network and System Security
• Start Date: Aug 21, 2017
• End Date: Aug 23, 2017
• Online Publication Date: Jul 26, 2017
• Publication Date: 2017
• Deposit Date: Nov 4, 2021
• Publisher: Springer
• Pages: 427-438
• Series Title: Lecture Notes in Computer Science
• Series Number: 10394
• Series ISSN: 1611-3349
• Book Title: Network and System Security: 11th International Conference, NSS 2017, Helsinki, Finland, August 21–23, 2017, Proceedings
• ISBN: 978-3-319-64700-5
• DOI: https://doi.org/10.1007/978-3-319-64701-2_31
• Public URL: http://researchrepository.napier.ac.uk/Output/2809240