Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge

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 need for multimodal detection. 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. In this paper, 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 real-world multimedia datasets collected from Weibo and Twitter. The proposed method outperforms the state-of-the-art methods for identifying fake news in terms of accuracy by 9% on average.