Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
It addresses fake news detection for specific high-impact domains like politics and health, where misinformation can have serious real-world consequences, though it is incremental as it builds on existing multi-domain methods.
The paper tackles the seesaw problem in multi-domain fake news detection, where improving some domains hurts others, by proposing DITFEND, which uses domain- and instance-level transfer to enhance performance in specific target domains, with offline experiments showing effectiveness and online experiments yielding additional improvements over base models.
Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.