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The wide dissemination of rumor is increasingly threatening both individuals and society. In this paper, we propose a multi-domain rumor detection method based on sentiment features and attention mechanism. This method uses sentiment analysis technology to extract sentiment features from text, and uses attention mechanism to weight text features and domain features to detect rumor. Experimental results show that the proposed method has achieved good results on data sets from multiple fields and has good generalization performance.

This paper addresses the limitations of existing rumor detection methods that heavily rely on single or local features, which restrict their ability to capture comprehensive and detailed characteristics of rumors. The main objective of this study is to enhance the efficiency of rumor detection. To achieve this, we propose a novel approach that integrates user attributes, comment structure, and propagation models, introducing the concept of ubiquitous relationships for messages in social networks. We construct a Tweet-word-user ubiquitous relationship network using a propagation model and further leverage the Graph Convolutional Neural Network (GCN) to enhance semantic features. Consequently, we present a novel rumor detection model, the Ubiquitous Relationship-based Graph Convolutional Neural Network (U-GCN), which effectively combines user, text, and comment features within a unified framework, while also enhancing semantic features from the source post for comprehensive detection. Extensive experiments are conducted on two publicly available Twitter Datasets. The results demonstrate that our proposed U-GCN model achieves an accuracy rate of above 0.9, outperforming methods that solely consider single or local features. Our findings highlight the effectiveness of leveraging ubiquitous relationships in rumor detection.

Aerial ladder fire truck is a kind of fire-fighting truck which is used for rescue of people and goods from high altitude locations. The aerial ladders of the fire truck are typically nested U-shaped truss structures manufactured with thin-walled steel by welding. Compared with close-shaped truss structures, their stiffnesses are relatively low. This means that they are quite slender structures, and they will deflect quite significantly due to their own weight and the working loads applied. If the original design of the ladder is straight, then the ladder will deform to a ‘fishing-rod’ shape in the air. The consequent ‘bent-down’ shape of the deformed ladder causes significant inconvenience during rescuing operations. To deal with this issue, a practical solution to the problem is that the ladder is manufactured with an appropriate uptilted curved shape, and it could deform to an approximately straight ladder when it is under certain desired working condition. In this paper, the effectiveness of the curved ladder design proposed in this paper has been validated by both finite element analysis and experimental results.