This paper proposes a new method called RL- RNN

that combines the power of Reinforcement Learning and

Recurrent Neural Networks in furthering malicious bot

detection. Malicious social bots on Twitter have emerged as a

significant threat to this platform, spreading disinformation,

manipulating public opinion, and influencing political events.

The existing traditional detection methods, such as Support

Vector Machines, are proven to be unable to deal with bot

behaviors dynam- ically and complicatedly. The proposed

model inculcates that URL features with frequency and

patterns of postings are hot indicators of bot activity. The RL

component enables the model to adjust dynamically to

changing bot behaviors. It also adapts the RNN type to capture

the sequential structure of the tweets with the associated URLs.

Thus, experimental results show that this approach using RL-

RNN hybrids significantly outperforms traditional SVM-based

methods by precision, recall, and overall detection accuracy.

The experimental results point out the RL- RNN model’s

ability to scalability in detecting evolving bot strategies on

Twitter. This makes the approach more effective as a

mitigation method for preventing malicious activities, thus

increasing security in social media systems