SCHOOL OF COMPUTER SCIENCE

The School of Computer Science is pleased to present…

 

 

 

Date: Tuesday February 25, 2020

Time: 1:00 pm – 2:00 pm 

Location: Lambton Tower, 3105

 

 

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 the 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 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 in terms of accuracy by 9% on average.

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MSc Thesis Proposal Announcement