We introduce DeepTrust, a reliable financial knowledge retrieval framework on Twitter to explain extreme price moves at speed, while ensuring data veracity using state-of-the-art NLP techniques.
The experimental results illustrate that this method is effective for the detection of Chinese fake news pertinent to the pandemic.Įxtreme pricing anomalies may occur unexpectedly without a trivial cause, and equity traders typically experience a meticulous process to source disparate information and analyze its reliability before integrating it into the trusted knowledge base. Between the TMLM-F and DA-PEC processes, we use Attention to capture the dependencies between words and generate corresponding vector representations. In the stage of encoding, Twice-Masked Language Modeling for Fine-tuning (TMLM-F) and Data Alignment that Preserves Edge Characteristics (DA-PEC) was proposed to extract the classification features of the Chinese Dataset.
In this paper, long text feature extraction network with data augmentation (LTFE) was proposed, which improves the learning performance of the classifier by optimizing the data feature structure. Besides, the accuracy of classification was also affected by the easy loss of edge characteristics in long text data. Facing shortages, imbalance, and nosiness, the current Chinese data set related to the epidemic has not helped the detection of fake news. Social media is rife with rumors and fake news, causing great damage to the Society. With the decrease in physical social contacts and the rise of anxiety on the pandemic, social media has become the primary approach for people to access information related to COVID-19. The spread of COVID-19 has had a serious impact on either work or the lives of people. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection.
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