In contrast with most earlier supervised ML approaches which are limited by the inability to effectively detect new types of bots, this paper proposes CALEB, a robust end-to-end proactive framework based on the Conditional Generative Adversarial Network (CGAN) and its extension, Auxiliary Classifier GAN (AC-GAN), to simulate bot evolution by creating realistic synthetic instances of different bot types. This work is motivated by the critical need to establish adaptive bot detection methods in order to proactively capture unseen evolved bots towards healthier OSNs interactions. Moreover, recent studies have shown that social bots evolve over time by reforming and reinventing unforeseen and sophisticated characteristics, making them capable of evading the current machine learning state-of-the-art bot detection systems. As highlighted by other researchers, most of these bots have malicious purposes and tend to mimic human behavior, posing high-level security threats on OSN platforms. The high growth of Online Social Networks (OSNs) over the last few years has allowed automated accounts, known as social bots, to gain ground. Finally, we make our data sets available for use by the research community. This suggests that the strategy employed may vary depending upon the level of imbalance in the data set, the amount of data available in a low resource setting, and the prevalence of context-specific spam vs. Because spam training data sets are notoriously imbalanced, we also investigate the impact of this imbalance and show that simple Bag-of-Words models are best with extreme imbalance, but a neural model that fine-tunes using language models from other domains significantly improves the F1 score, but not to the levels of domain-specific neural models. The neural network model outperforms the traditional models with an F1 score of 0.91. We then compare multiple traditional machine learning models and a neural network model that uses a pre-trained BERT language model to capture contextual features for identifying spam, both traditional and context-specific, using only content-based features. We show on different Twitter data sets that context-specific spam exists and is identifiable. Most studies assume that spam is context-neutral.
One form of commonly studied low-quality content is spam. Social media data has a mix of high and low-quality content.
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