Multi-view learning with distinguishable feature fusion for rumor detection

A syntactic evidence network model for fact verification

In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use the attention mechanism for fact verification, they have not considered imposing attentional constraints on important related words in the claim and evidence sentences, resulting in inaccurate attention for some irrelevant words. In this paper, we propose a syntactic evidence network (SENet) model which incorporates entity keywords, syntactic information and sentence attention for fact verification. The SENet model extracts entity keywords from claim and evidence sentences, and uses a pre-trained syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic information into the attention mechanism for language-driven word representation. In addition, the sentence attention mechanism is applied to obtain a richer semantic representation. We have conducted experiments on the FEVER and UKP Snopes datasets for performance evaluation. Our SENet model has achieved 78.69% in Label Accuracy and 75.63% in FEVER Score on the FEVER dataset. In addition, our SENet model also has achieved 65.0% in precision and 61.2% in macro F1 on the UKP Snopes dataset. The experimental results have shown that our proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification.

SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detection

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence of comments irrelevant to the discussion topic of the source post. To address this, we introduce a novel approach: the Structure-Aware Multilevel Graph Attention Network (SAMGAT) for rumor classification. SAMGAT employs a dynamic attention mechanism that blends GATv2 and dot-product attention to capture the contextual relationships between posts, allowing for varying attention scores based on the stance of the central node. The model incorporates a structure-aware attention mechanism that learns attention weights that can indicate the existence of edges, effectively reflecting the propagation structure of rumors. Moreover, SAMGAT incorporates a top-k attention filtering mechanism to select the most relevant neighboring nodes, enhancing its ability to focus on the key structural features of rumor propagation. Furthermore, SAMGAT includes a claim-guided attention pooling mechanism with a thresholding step to focus on the most informative posts when constructing the event representation. Experimental results on benchmark datasets demonstrate that SAMGAT outperforms state-of-the-art methods in identifying rumors and improves the effectiveness of early rumor detection.

Deep Learning for Combating Misinformation in Multicategorical Text Contents

Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional 'editorial office', placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work. As a result, social media has become an element of state security, as disinformation and fake news produced by malicious actors can manipulate readers, creating unnecessary debate on topics organically irrelevant to society. This causes a cascading effect, fear of citizens, and eventually threats to the state's security. Advanced data sensors and deep machine learning methods have great potential to enable the creation of effective tools for combating the fake news problem. However, these solutions often need better model generalization in the real world due to data deficits. In this paper, we propose an innovative solution involving a committee of classifiers in order to tackle the fake news detection challenge. In that regard, we introduce a diverse set of base models, each independently trained on sub-corpora with unique characteristics. In particular, we use multi-label text category classification, which helps formulate an ensemble. The experiments were conducted on six different benchmark datasets. The results are promising and open the field for further research.

Adaptive rotation attention network for accurate defect detection on magnetic tile surface

Defect detection on magnetic tile surfaces is of great significance for the production monitoring of permanent magnet motors. However, it is challenging to detect the surface defects from the magnetic tile due to these issues: 1) Defects appear randomly on the surface of the magnetic tile; 2) the defects are tiny and often overwhelmed by the background. To address such problems, an Adaptive Rotation Attention Network (ARA-Net) is proposed for defect detection on the magnetic tile surface, where the Adaptive Rotation Convolution (ARC) module is devised to capture the random defects on the magnetic tile surface by learning multi-view feature maps, and then the Rotation Region Attention (RAA) module is designed to locate the small defects from the complicated background by focusing more attention on the defect features. Experiments conducted on the MTSD3C6K dataset demonstrate the proposed ARA-Net outperforms the state-of-the-art methods, further providing assistance for permanent magnet motor monitoring.

A Rumor Detection Model Incorporating Propagation Path Contextual Semantics and User Information

Currently, social media is full of rumors. To stop rumors from spreading further, rumor detection has received increasing attention. Recent rumor detection methods treat all propagation paths and all nodes on the paths as equally important, resulting in models that fail to extract the key features. In addition, most methods ignore user features, leading to limitations in the performance improvement of rumor detection. To address these problems, we propose a Dual-Attention Network model on propagation Tree structures named DAN-Tree, where a node-and-path dual-attention mechanism is designed to organically fuse deep structure and semantic information on the propagation structures of rumors, and path oversampling and structural embedding are employed to enhance the learning of deep structures. Finally, we deeply integrate user profiles into the propagation trees in DAN-Tree, thus proposing the DAN-Tree++ model to further improve performance. Empirical studies on four rumor datasets have shown that DAN-Tree outperforms the state-of-the-art rumor detection models learning on propagation structures, and the results on two datasets with user information validate the superior performance of DAN-Tree++ over other models using both user profiles and propagation structures. What's more, DAN-Tree, especially DAN-Tree++, has achieved the best performance on early detection tasks.

Research on reform and breakthrough of news, film, and television media based on artificial intelligence

With the development of technology, news media and film and television media are spreading faster and faster, and at the same time, the spread of rumors is also accelerated. This article briefly describes the application of artificial intelligence in news media and film and television media using a back-propagation neural network (BPNN) algorithm to reform refutation of rumors in news media and film and television media, and compared it with K-means and support vector machine algorithms in simulation experiments. The results showed that the BPNN-based rumor recognition model had better recognition performance and shorter recognition time; it was more accurate in recognizing Weibo texts that were complete and faster in recognizing bullet screen comments that were short; the BPNN-based rumor recognition model also had the lowest false detection cost and performed stably when being used in actual Weibo platform and bullet screen video website.