Zeng Kaqian, Li Zhao, Wang Xiujuan
College of Computer Science, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2025 Jul 4;25(13):4179. doi: 10.3390/s25134179.
The proliferation of malicious social bots poses severe threats to cybersecurity and social media information ecosystems. Existing detection methods often overlook the semantic value and emotional cues conveyed by emojis in user-generated tweets. To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal detection framework that integrates semantic enhancement, sentiment analysis, and multi-dimensional feature modeling. Specifically, we first establish emoji-text mapping relationships using the Emoji Library, leverage GPT-4 to improve textual coherence, and generate tweet embeddings via RoBERTa. Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. Finally, a Relational Graph Convolutional Network (RGCN) is employed to model heterogeneous social topology for robust bot detection. Experimental results on the TwiBot-20 benchmark dataset demonstrate that our method achieves a superior accuracy of 87.46%, significantly outperforming baseline models and validating the effectiveness of emoji-driven semantic and sentiment enhancement strategies.
恶意社交机器人的扩散对网络安全和社交媒体信息生态系统构成了严重威胁。现有的检测方法往往忽视了用户生成的推文表情符号所传达的语义价值和情感线索。为了弥补这一差距，我们提出了ESA-BotRGCN，这是一个表情符号驱动的多模态检测框架，集成了语义增强、情感分析和多维度特征建模。具体来说，我们首先使用表情符号库建立表情符号-文本映射关系，利用GPT-4提高文本连贯性，并通过RoBERTa生成推文嵌入。随后，提取七个基于情感的特征，以量化机器人账户和人类账户在情感表达模式上的统计差异。进一步设计了一种注意力门控机制，将这些情感特征与用户描述、推文内容、数值属性和分类特征动态融合。最后，采用关系图卷积网络（RGCN）对异构社会拓扑进行建模，以实现强大的机器人检测。在TwiBot-20基准数据集上的实验结果表明，我们的方法实现了87.46%的卓越准确率，显著优于基线模型，并验证了表情符号驱动的语义和情感增强策略的有效性。
