Multi-Source Selective Transfer Learning for Fake News Detection in New Event

Automatically detecting fake news has become increasingly necessary. Conventional approaches to fake news detection (FND) require a large number of training instances, which are not available in the scenario of new event FND (NEFND). More advanced methods address this problem through domain adaption (DA) to improve the overall performance of all events, or by transferring knowledge from source events. However, these methods either lack a target-oriented design or fail to perform effective transfer due to data scarcity in new events. This work focuses on the NEFND problem and proposes a multi-source selective transfer learning approach. Specifically, an integrated learner is built to make decisions, and an event-level transferability generator is designed to select more transfer-worthy source events, so as to achieve event-level selective transfer. Additionally, a two-stage training algorithm with a re-weighting optimization mechanism is also designed to highlight more transferable source instances, so as to achieve instance-level selective transfer and improve the performance on the target event. Experiments on the real-world multi-event fake news dataset that simulates the NEFND scenario are conducted to evaluate the effectiveness and superiority of the proposed approach.