List of Articles Fake News

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  1 - Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news
  arefeh esmaili Saeed Farzi
  For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has bec More

  For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news. Manuscript profile
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  2 - Designing a dynamic model of brand equity with a focus on fake news and customer knowledge at Coca-Cola
  Davood  ariannejad Alireza  Pouya Hadi  Bastam Ali  Hosseinzadeh
  Objective: The purpose of this research is to design a dynamic model of brand equity focusing on fake news and customer knowledge. Methodology: The research approach used is mixed and the time period of simulation in this research was (from 2025-2020). Library sources More

  Objective: The purpose of this research is to design a dynamic model of brand equity focusing on fake news and customer knowledge. Methodology: The research approach used is mixed and the time period of simulation in this research was (from 2025-2020). Library sources have been used to collect information in the stages of identifying variables, expressing dynamic hypothesis, conceptualizing the system, and forming initial cause and effect diagrams; In order to clarify how the variables affect each other, formulating relationships and verifying the validity of the final model of system dynamics from interviews with experts in the food industry and Mashhad Co. ¬ Production of behavior was used. Findings: According to the results of the research, brand equity has a dynamic nature, and all the dimensions and components affecting it can be changed over time based on different policies, and also the repetition and spread of fake news about an organization and its products can reduce brand equity. and the amount of trust of customers and the credibility of the organization decreases with the repetition of fake news. Conclusion: The investment of the organization in order to increase the customer's knowledge about the products and remove the misconceptions caused by fake news can increase the trust in the brand and lead to the neutralization of the effect of fake news on the brand value. Manuscript profile
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  3 - Image Fake News Detection using Efficient NetB0 Model
  Yasmine Almsrahad Nasrollah  Moghaddam Charkari
  10.61186/jist.40976.12.45.41
  Today, social networks have become a prominent source of news, significantly altering the way people obtain news from traditional media sources to social media. Alternatively, social media platforms have been plagued by unauthenticated and fake news in recent years. How More

  Today, social networks have become a prominent source of news, significantly altering the way people obtain news from traditional media sources to social media. Alternatively, social media platforms have been plagued by unauthenticated and fake news in recent years. However, the rise of fake news on these platforms has become a challenging issue. Fake news dissemination, especially through visual content, poses a significant threat as people tend to share information in image format. Consequently, detecting and combating fake news has become crucial in the realm of social media. In this paper, we propose an approach to address the detection of fake image news. Our method incorporates the error level analysis (ELA) technique and the explicit convolutional neural network of the EfficientNet model. By converting the original image into an ELA image, it is possible to effectively highlight any manipulations or discrepancies within the image. The ELA image is further processed by the EfficientNet model, which captures distinctive features used to detect fake image news. Visual features extracted from the model are passed through a dense layer and a sigmoid function to predict the image type. To evaluate the efficacy of the proposed method, we conducted experiments using the CASIA 2.0 dataset, a widely adopted benchmark dataset for fake image detection. The experimental results demonstrate an accuracy rate of 96.11% for the CASIA dataset. The results outperform in terms of accuracy and computational efficiency, with a 6% increase in accuracy and a 5.2% improvement in the F-score compared with other similar methods. Manuscript profile