<![CDATA[The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is employed for feature extraction to capture deep contextual semantics, followed by classification through a deep neural network (DNN) to effectively distinguish between human users and bots. The proposed model is evaluated against established baselines using standard performance metrics. Experimental results demonstrate that DeepBot achieves superior performance with an accuracy of 92.82%, precision of 91.24%, and recall of 91.78%, confirming its effectiveness in enhancing the reliability of bot detection in social media trend analysis.]]>
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