The field of fake news and cybersecurity detection is rapidly evolving, with a focus on developing more robust and accurate detection methods. Recent research has highlighted the importance of adapting to emerging threats, such as large language models (LLMs) and adversarial attacks. The development of novel frameworks and techniques, such as group-adaptive adversarial training and structure-aware propagation generation, has shown promise in improving detection accuracy and robustness. Additionally, the integration of multi-modal signals, such as IP addresses and network-level signals, has been explored to enhance detection capabilities. Noteworthy papers in this area include: The paper on Group-Adaptive Adversarial Learning for Robust Fake News Detection Against Malicious Comments, which introduces a novel approach to improving the robustness of fake news detection models. The paper on Towards Real-Time Fake News Detection under Evidence Scarcity, which proposes a framework for real-time fake news detection that dynamically adapts its decision-making process according to the assessed sufficiency of available evidence.