The rapid growth of information dissemination on social networks such as Facebook and Twitter, along with the widespread of mobile devices, has intensified the need for early yet accurate rumor detection methods. While existing hybrid machine learning models integrating natural language processing (NLP) and graph neural networks (GNNs) show promising results but often lack effective integration of diverse features and temporal modeling. To overcome these limitations, we introduce MTAS, a novel framework designed to detect rumors effectively by generating a multiview of social data encompassing semantic, inner, global, and temporal features. Specifically, MTAS models social network data into a comprehensive graph structure representing the global relationship among all tweets, words, and users. Through this structure, the framework processes and analyzes propagation patterns, semantic content, and temporal features. Additionally, it employs a subgraph-level attention mechanism to combine the extracted representations. By explicitly modeling temporal dynamics and enhancing feature fusion, MTAS achieves superior performance. Extensive experiments on two datasets collected from Twitter, Twitter15 and Twitter16, demonstrate that MTAS outperforms state-of-the-art methods, achieving accuracies of 92.9% and 94.6%, respectively, compared to 91.1% and 93.7% for existing best-performing models. Notably, MTAS excels in early-stage rumor detection, achieving over 90% accuracy within the first 8 h of rumor propagation, a crucial step for mitigating the spread of misinformation.