Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science

First Advisor

Ziad Kobti


Cultural algorithm, fake news detection, Segmentation process, Sentiment analysis




The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. This makes detection of fake news an extremely important challenge. 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 the aim of 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 thesis research, we present a novel approach using a Cultural Algorithm with situational and normative knowledge to detect fake news using both text and images. The proposed model’s principal innovation is to use the power of natural language processing like sentiment analysis, segmentation process for feature extraction, and optimizing it with a Cultural algorithm. Then the representations from both modalities are fused, which is finally used for classification. An extensive set of experiments is 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