Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of false information and posing significant challenges to platform regulation. To better understand and counter these threats, we innovatively design BotSim, a malicious social botnet simulation powered by LLM. BotSim mimics the information dissemination patterns of real-world social networks, creating a virtual environment composed of intelligent agent bots and real human users. In the temporal simulation constructed by BotSim, these advanced agent bots autonomously engage in social interactions such as posting and commenting, effectively modeling scenarios of information flow and user interaction. Building on the BotSim framework, we construct a highly human-like, LLM-driven bot dataset called BotSim-24 and benchmark multiple bot detection strategies against it. The experimental results indicate that detection methods effective on traditional bot datasets perform worse on BotSim-24, highlighting the urgent need for new detection strategies to address the cybersecurity threats posed by these advanced bots.

Identifying bots on social media has become a crucial and challenging task for regulating online discourse. Existing detection methods primarily focus on individual account-level information, identifying potential threats by detecting inconsistencies between genuine humans and anomalous bots in personal profiles, textual content, and social relationships. However, these approaches generally overlook the coordinated behavior characteristics inherent in groups of bot accounts. To address this research gap, we propose a novel Bot detection network based on Coordinated Group Perception (BotCGP), which enhances bot identification performance by uncovering the collective coordinated features among bot groups. Specifically, our method jointly models account profiles, textual content, and social relationships using a Student’s t-distribution kernel function and a differentiable modularity function to capture potential coordinated characteristics. Experimental results demonstrate that BotCGP significantly outperforms existing methods in bot detection across three real-world X/Twitter datasets. Our code is available at https://github.com/QQQQQQBY/BotCGP.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

With social bots evolving continually, the new bots post highly anthropomorphic posts to evade detection. For post content information, existing methods choose to extract features from single posts or use the rough characterization of the pretraining language model for bot detection. However, the evolution of bots has made them better at camouflage in previous posts processing techniques. According to our observation, the purpose of bot posting is to publicize different contents in different periods, and its posting often shows abnormal interest changes, while human posting is to express hobbies and daily life, and its interest changes are relatively stable. Therefore, we extract the interest changes between multiple posts published by users for bot detection. In this paper, we propose a social Bot detection model based on the Window Strategy(BotWS). Specifically, we first employ the window strategy to obtain the user posting windows, each containing multiple posts. Then, we extract the interest changes between the posting windows by multi-head attention mechanism. Finally, we embed the interest changes information into the user representation and construct a heterogeneous graph classification module to classify. We conduct experiments on three challenging datasets. Results indicate that our approach achieves state-of-the-art.

The detection of social bots has become a critical task in maintaining the integrity of social media. With social bots evolving continually, they primarily evade detection by imitating human features and engaging in interactions with humans. To reduce the impact of social bots imitating human features, also known as feature camouflage, existing methods mainly utilize multi-modal user information for detection, especially GNN-based methods that utilize additional topological structure information. However, these methods ignore relation camouflage, which involves disguising through interactions with humans. We find that relation camouflage results in both homophilic connections formed by nodes of the same type and heterophilic connections formed by nodes of different types in social networks. The existing GNN-based detection methods assume all connections are homophilic while ignoring the difference among neighbors in heterophilic connections, which leads to a poor detection performance for bots with relation camouflage. To address this, we propose a multi-modal social bot detection method with learning homophilic and heterophilic connections adaptively (BothH for short). Specifically, firstly we determine whether each connection is homophilic or heterophilic with the connection classifier, and then we design a novel message propagating strategy that can learn the homophilic and heterophilic connections adaptively. We conduct experiments on the mainstream datasets and the results show that our model is superior to state-of-the-art methods.