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Both real and fake news in various domains, such as politics, health, andentertainment are spread via online social media every day, necessitating fakenews detection for multiple domains. Among them, fake news in specific domainslike politics and health has more serious potential negative impacts on thereal world (e.g., the infodemic led by COVID-19 misinformation). Previousstudies focus on multi-domain fake news detection, by equally mining andmodeling the correlation between domains. However, these multi-domain methodssuffer from a seesaw problem: the performance of some domains is often improvedat the cost of hurting the performance of other domains, which could lead to anunsatisfying performance in specific domains. To address this issue, we proposea Domain- and Instance-level Transfer Framework for Fake News Detection(DITFEND), which could improve the performance of specific target domains. Totransfer coarse-grained domain-level knowledge, we train a general model withdata of all domains from the meta-learning perspective. To transferfine-grained instance-level knowledge and adapt the general model to a targetdomain, we train a language model on the target domain to evaluate thetransferability of each data instance in source domains and re-weigh eachinstance's contribution. Offline experiments on two datasets demonstrate theeffectiveness of DITFEND. Online experiments show that DITFEND bringsadditional improvements over the base models in a real-world scenario.

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer