Fake news has already become a severe problem on social media, with substantially more detrimental impacts on society than previously thought. Research on multi-modal fake news detection has substantial practical significance since online fake news that includes multimedia elements are more likely to mislead users and propagate widely than text-only fake news. However, the existing multi-modal fake news detection methods have the following problems: 1) Existing methods usually use traditional CNN models and their variants to extract image features, which cannot fully extract high-quality visual features. 2) Existing approaches usually adopt a simple concatenate approach to fuse inter-modal features, leading to unsatisfactory detection results. 3) Most fake news has large disparity in feature similarity between images and texts, yet existing models do not fully utilize this aspect. Thus, we propose a novel model (TGA) based on transformers and multi-modal fusion to address the above problems. Specifically, we extract text and image features by different transformers and fuse features by attention mechanisms. In addition, we utilize the degree of feature similarity between texts and images in the classifier to improve the performance of TGA. Experimental results on the public datasets show the effectiveness of TGA^*. ^* Our code is available at https://github.com/PPEXCEPED/TGA.

With the rise of social media, the rapid spread of rumors online has resulted in numerous negative effects on society and the economy. The methods for rumor detection have attracted great interest from both academia and industry. Given the widespread effectiveness of contrastive learning, many graph contrastive learning models for rumor detection have been proposed by using the event propagation structure as graph data. However, the existing contrastive models usually treat the propagation structure of other events similar to the anchor events as negative samples. While this design choice allows for discriminative learning, on the other hand, it also inevitably pushes apart semantically similar samples and, thus, degrades model performance. In this article, we propose a novel propagation fusion model called propagation structure fusion model based on node-level contrastive learning (PFNC) for rumor detection based on node-level contrastive learning. PFNC first obtains three augmented propagation structures by masking the text of each node in the propagation structure randomly and perturbing some edges in the propagation structure based on the importance of edges. Then, PFNC applies the node-level contrastive learning method between every two augmented propagation structures to prevent the samples with similar propagation structure from far away. Finally, a convolutional neural network (CNN)-based model is proposed to capture the relevant information that is consistent and supplementary among three augmented propagation structures by regarding the propagation structure of the event as a color picture, three augmented propagation structures as color channels, and each node as a pixel. The experimental results on real datasets show that the PFNC significantly outperforms the state-of-the-art models for rumor detection.

We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.