Bot detection using a machine learning adaptive transfer approach
Authors
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Daniyal BaigComputer Science, Lahore Garrison University, Lahore, Pakistan
Keywords:
Machine learning, Supervised, KNN, Random forest, Bot detection, Decision tree, ANNAbstract
Bot presence on social networking applications creates a lot of distress for the population. Authenticity and reliability of the content can only be checked with the embedding of latest technologies like machine learning and deep learning or broadly generalized as artificial intelligence. The proposed method detects the bots on social networking applications using a machine learning supervised and unsupervised methodologies. The main contribution of the research is the novelty factor of embedding supervised and unsupervised algorithms with a better accuracies and precision. The proposed method will improve the social networking applications by identifying the inconsistent data. The proposed research uses decision tree, random forest, KNN and ANN for predicting social media bot presence. Machine learning approach uses Xboost and Gboost methods to improve the accuracies and precision. The comparative analysis proves the significance of the proposed system. The proposed model achieved 97 % accuracy with 0.012 learning loss.
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Copyright (c) 2024 Daniyal Baig
This work is licensed under a Creative Commons Attribution 4.0 International License.
