The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. A fake news item is usually created by manipulating photos, text, or videos that indicate the needformultimodaldetection.Researchers are building detection algorithms with an aim for high accuracy as this will have a massive impact on the prevailing social and political issues. A shortcoming of existing strategies for identifying fake news is their inability to learn a feature representation of multimodal (textual+visual) information.
Develop a machine learning program to identify when a news source may be producing fake news. We aim to use a corpus of labeled real and fake new articles to build a classifier that can make decisions about information based on the content from the corpus. The model will focus on identifying fake news sources, based on multiple articles originating from a source. we present a novel approach using a Cultural Algorithm with situational and normative knowledge to detect fake news using both text and images. An extensive set of experiments have been carried out on realworld multimedia datasets collected from Weibo and Twitter.
Given the sparse availability of structured multimedia data, we make use of two standard datasets to evaluate our architecture for fake news detection.Our model training is carried out on two datasets, i.e. Weibo dataset and Twitter dataset, which are publicly available. These are the only databases accessible to the best of our knowledge that has combined picture and textual content.