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.
翻译:政治、卫生和娱乐等各个领域的真实和假新闻每天都通过在线社交媒体传播,因此有必要为多个领域进行假新闻探测。其中,政治和健康等特定领域的假新闻可能对真实世界产生更严重的潜在负面影响(例如,由COVID-19错误信息主导的温和型态)。先前的研究侧重于多域假新闻探测,同样通过采矿和模拟各域间的相关性。然而,这些多域方法存在一个直觉问题:一些域的性能往往在损害其他域性能的代价下得到改善,这可能导致特定域的不满意性业绩。为了解决这一问题,我们提议了一个用于Fake新闻探测的多域和实级转移框架(DITFEND),该框架可以改善特定目标域的性能。为了传播粗略的ENDR域级知识,我们从元学习的角度来培训一个包含所有域数据数据的一般模型。为了转移精细化实例级知识,并将一般模型调整到一个目标域域域,这可能导致在特定域内取得不尽满意的业绩。我们要在目标域内对目标域域域域域进行真正的实验,我们训练一个语言模型,在显示每个域域域域域域域域域的交付数据模型。