SAMS: Human-in-the-loop Approach to Combat the Sharing of Digital Misinformation

Shaban Shabani, Zarina Charlesworth, Maria Sokhn, Heiko Schuldt
In Proceedings
Appears in
Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)
Stanford University, Palo Alto, California, USA (held virtually)

Spread of online misinformation is an ubiquitous problem especially in the context of social media. In addition to the impact on global health caused by the current COVID-19 pandemic, the spread of related misinformation poses an additional health threat. Detecting and controlling the spread of misinformation using algorithmic methods is a challenging task. Relying on human fact-checking experts is the most reliable approach, however, it does not scale with the volume and speed with which digital misinformation is being produced and disseminated. In this paper, we present the SAMS Human-in-the-loop (SAMS-HITL) approach to combat the detection and the spread of digital misinformation. SAMS-HITL leverages the fact-checking skills of humans by providing feedback on news stories about the source, author, message, and spelling. The SAMS features are jointly integrated into a machine learning pipeline for detecting misinformation. First results indicate that SAMS features have a marked impact on the classification as it improves accuracy by up to 7.1%. The SAMS-HITL approach goes one step further than the traditional human-in-the-loop models in that it helps raising awareness about digital misinformation by allowing users to become self fact-checkers.