Several machine learning techniques, including supervised, unsupervised and reinforcement learning, have been proposed to detect bots in Twitter. These techniques mainly use a limited number of features extracted for identifying automated accounts at account-level.
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Apr 20, 2021 · In the second method, a deep learning architecture is proposed to identify whether tweets have been posted by real users or generated by bots.
Mar 6, 2023 · Several machine learning techniques, including supervised, unsupervised and reinforcement learning, have been proposed to detect bots in ...
J. Kaubiyal, A.K. Jain, A feature based approach to detect fake profiles in twitter, in: Proceedings of the 3rd International Conference on Big Data and ...
Jun 23, 2023 · In this research, we used three different machine learning techniques—a Decision Tree, a Random Forest, and a Multinomial. Naive Bayes—to ...
Mar 17, 2024 · Malicious twitter Bots detection is required to detect real users from fraudulent users because it leads to spreading of spam messages and ...
This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate ...
Scalable Learning Framework for Detecting New Types of Twitter ...
www.ncbi.nlm.nih.gov › PMC11014207
Apr 2, 2024 · This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam ...
Jun 20, 2017 · In this paper, we proposed a novel technique based on deep-learning technique to address the above challenges. The syntax of each tweet will be ...
ABSTRACT: Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating.