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README.md

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 <h2 align="center"> RST-Annotated Dataset for Multilingual Fake News Detection </h2>  
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+<p align="justify">Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. Furthermore, there are reliable cues for detecting deception, and the belief that liars give off cues that may indicate their deception is near-universal. Therefore, corroborating that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people’s cognitive mechanisms have promising guidance for deception detection, we propose the first multilingual corpus composed of fake and true news annotated with the Rhetorical Structure Theory (RST) framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual RST-annotated corpora for fake news detection.
 
 <h2 align="left"> FUNDING </h2>