Information diffusion analysis in online social networks based on deep representation learning

With the emergence of online social networks (OSNs), the way people create and share information has changed, which becomes faster and broader than traditional social media. Understanding how information (both good and harmful) spreads through OSNs, as well as what elements drive the success of information diffusion, has significant implications for a wide range of real-world applications. In this thesis, we conduct research to analysis the diffusion of information in OSNs via using deep representation learning. Specifically, we aim to develop deep learning- based models to solve two specific tasks, i.e., information cascades modeling and rumor detection. Algorithms and the Foundations of Software technolog