About Lecture: Although the past decade has witnessed the great rise of Artificial Intelligence (AI), it is still broadly acknowledged that the current AI approaches have systematic weaknesses and blind spots in many applications. There is strong motivation to develop hybrid collective intelligence systems which tap into the complementary strengths of humans and machines and the power of collective intelligence. This report is going to introduce hybrid collective intelligence and show how to apply it in the task of false news detection.

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a hybrid framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of such hybrid approach and the superior performance of the proposed framework in comparison with the benchmark methods.