TL-PBot : Twitter bot profile detection using transfer learning based on DNN model

Abstract

Online social networks (OSNs) have reduced global boundaries, with Twitter enabling perspective sharing. Bot profile-propagated false information misuse raises serious concerns. Considering this issue, we present our research on classifying Twitter accounts as “human” or “bot” using deep neural networks and transfer learning. Our proposed approach, TL-PBot, stands for bot profile detection using transfer learning. The TL-PBot framework utilizes Twitter account metadata such as follower count. Our TL-PBot also incorporates text data from the Twitter description field as a feature. Word representation of the text data is achieved using Global Vectors (GloVe), a pre-trained model. By employing user profile-based features, we significantly reduce the overhead of feature engineering. The hybrid nature of the model enables it to effectively handle mixed-type features, including text, binary, and numerical data. We design the network using long-short-term memory (LSTM) units. DNN model layers were trained, and the weights of the pre-trained model layers were frozen to apply the transfer learning, resulting in reduced training time and improved bot profile detection accuracy. The performance of the proposed TL-PBot is evaluated using publicly available datasets. The proposed approach is trained and tested on the same datasets and further evaluated on the validation datasets that were not used in the training phase, which is also a novelty in our approach. Comparative analysis with state-of-the-art approaches demonstrates that the TL-PBot approach achieves a higher accuracy of 98.07%, while excelling in precision of 99%, recall of 98%, f measure of 98.32%, and AUC of 0.99. Employing the transfer learning strategy resulted in an accelerated detection rate of 5.04 milliseconds, attesting to the effectiveness of this approach in enhancing computational efficiency.