The extensive use of social media platforms has promoted the rapid spread of fake news on the internet, such as fake reviews, rumors, and propaganda. Although these terminologies have different objectives, they share the aim of causing harm in the form of fake news. This study presents an Arabic fake news detection framework to overcome the widespread fake news phenomenon. The proposed framework introduces the first Arabic fake news dataset compiled by passing through strict guidelines to produce fake articles composed of humans and the generative pre-trained transformer (GPT). First, we performed human-based experiments to evaluate the ability of humans to distinguish real news articles from fake news articles. Our findings reveal that humans could roughly identify half of the fake articles from humans or GPT, raising concerns about their ability to detect fake news. This highlights the growing concern surrounding fake news, especially because GPT demonstrates the ability to generate fake news that closely resembles human-created content, further amplifying the issue. To address this issue, we performed the same task using Deep Learning (DL) and transformer-based methods with different word embeddings. Across all the employed models, the study revealed that the innovative transformer-based model, ARBERT, outperformed the DL models, reaching an accuracy of 78% in classifying real and fake news generated by humans and GPT. The findings suggest effective techniques for addressing and resolving this issue.

In this article, we propose novel approaches to deal with the problem of burstiness, the challenge of count-data sparseness, and the curse of dimensionality. We introduce a smoothed generalized Dirichlet distribution that is a smoothed variant of the generalized Dirichlet distribution and a generalization of the smoothed Dirichlet. We provide different learning methods based on mixture models and agglomerative clustering-based geometrical information: Kullback–Leibler divergence, Fisher metric, and Bhattacharyya distance. Moreover, we show that the new smoothed generalized Dirichlet could be considered as a prior to the multinomial, which generates a new distribution for count data that we call the smoothed generalized Dirichlet multinomial. In particular, we present an approximation based on Taylor series expansion for better performance and optimized running time in the case of high-dimensional count data. The proposed models are evaluated through two emotion detection applications: disaster-tweet-related emotions and pain intensity estimation. Experiments show the efficiency and the robustness of our approaches when dealing with texts, videos, and images.

The rapid dissemination of fake news in the digital era has become a pressing concern. The ease of generating and manipulating fake content, including images, text, audio, and videos, has significantly fueled the spread of misinformation on social media platforms. These platforms often lack rigorous editorial scrutiny, exacerbating this problem. Although recent studies have explored multimodal fake news detection to learn shared representations of textual and visual information, they often learn discrete latent representations, merely concatenations of multimodal features. Simple concatenation or summation operations hinder the dynamic interaction of multimodal features. Furthermore, most models rely on additional subtasks, such as reconstruction and event discrimination. The performance of these models depends heavily on subtasks, which can be mathematically complex and time-consuming. This reliance limits the ability of researchers to explore different modeling assumptions freely. This study introduces a novel approach that integrates a probabilistic algorithm with a deep neural network to effectively capture the uncertainties and diversities in the shared latent representation of multimodal data. Specifically, our model utilizes continuous latent representations by leveraging a smoothed Dirichlet distribution, facilitating the identification of shared hidden patterns across textual and visual modalities. In addition, our model demonstrates the powerful properties of generative models when integrated with neural network models. Our results underscore the potential of integrating a probabilistic algorithm with a deep neural network to address the challenges of fake news detection in a multimodal setting. To support further research and reproducibility, we made the code related to this work publicly accessible.