|
Abstract
|
Fake news detection refers to the process of distinguishing false or manipulated information from real news. This process involves utilizing artificial intelligence, machine learning, and graph-based models to analyze data and identify misleading patterns. In this regard, multi-modal features serve as sources of diverse information, such as text, images, audio, and video, enhancing the accuracy and reliability of the analysis. Additionally, employing adversarial graph learning frameworks can contribute to more precise fake news detection, as these frameworks are capable of countering misinformation and reducing classification errors [1].
The use of multi-modal features in fake news detection allows for a more comprehensive analysis of information. These features encompass text analysis, image analysis, and the examination of audio-visual data, facilitating the creation of a more holistic representation of input data. Furthermore, adversarial graph learning frameworks leverage graph structures for data processing, enabling the identification of complex relationships among various information sources and the detection of fake news patterns [2]. Given the increasing complexity of digital information, fake news detection has become essential for preserving the accuracy and integrity of information in different societies. Advanced methods such as multi-modal features and adversarial graph learning play a crucial role in strengthening this process, helping users access reliable information with greater confidence [3].
Fake news detection using multi-modal features and adversarial graph learning frameworks is one of the latest approaches to combating misinformation in the digital world. This method comprehensively analyzes information from various dimensions, such as text, images, and videos. Multi-modal features facilitate the analysis of diverse data and enhance the accuracy and reliability of the detection process by integrating different information sources [4]. Additi
|