ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information

November 16, 2023 ยท Declared Dead ยท ๐Ÿ› 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)

๐Ÿ‘ป CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Sabrine Amri, Henri-Cedric Mputu Boleilanga, Esma Aรฏmeur arXiv ID 2311.10784 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.LG, cs.SI Citations 3 Venue 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) Last Checked 5 months ago
Abstract
ExFake is an explainable fake news detection system based on content and context-level information. It is concerned with the veracity analysis of online posts based on their content, social context (i.e., online users' credibility and historical behaviour), and data coming from trusted entities such as fact-checking websites and named entities. Unlike state-of-the-art systems, an Explainable AI (XAI) assistant is also adopted to help online social networks (OSN) users develop good reflexes when faced with any doubted information that spreads on social networks. The trustworthiness of OSN users is also addressed by assigning a credibility score to OSN users, as OSN users are one of the main culprits for spreading fake news. Experimental analysis on a real-world dataset demonstrates that ExFake significantly outperforms other baseline methods for fake news detection.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Computation & Language

๐ŸŒ… ๐ŸŒ… Old Age

Attention Is All You Need

Ashish Vaswani, Noam Shazeer, ... (+6 more)

cs.CL ๐Ÿ› NeurIPS ๐Ÿ“š 166.0K cites 9 years ago

Died the same way โ€” ๐Ÿ‘ป Ghosted