论文标题
MIDAS：虚拟新闻检测的多集成域自适应监督
MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection
论文作者
论文摘要
在过去的几年中，Covid-19的相关错误信息和虚假新闻创造了“流行病”。这种错误信息表现出概念漂移，随着时间的推移，假新闻的分布会随着时间的流逝而变化，从而降低了以前训练的模型以进行虚假新闻检测。鉴于在多个域上训练的一组假新闻模型，我们提出了一个自适应决策模块，以选择新样本的最佳拟合模型。我们提出了MIDAS，这是一种用于假新闻检测的多域适应性方法，将现有模型与新样本的相关性排名。 MIDAS包含2个组件：Doman-Invariant编码器和一个自适应模型选择器。 MIDAS将多个预训练和微调的模型与其训练数据集成在一起，以创建域不变的表示。然后，MIDAS使用不变嵌入空间的本地Lipschitz平滑度来估计每个模型与新样本的相关性。排名较高的模型提供了预测，并且排名较低的模型弃权。我们评估了MIDAS对使用9个假新闻数据集漂移数据的概括，每个数据都从不同的域和模式获得。 MIDAS在多域改编方面取得了新的最新性能，用于分发假新闻分类。
COVID-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reducing effectiveness of previously trained models for fake news detection. Given a set of fake news models trained on multiple domains, we propose an adaptive decision module to select the best-fit model for a new sample. We propose MiDAS, a multi-domain adaptative approach for fake news detection that ranks relevancy of existing models to new samples. MiDAS contains 2 components: a doman-invariant encoder, and an adaptive model selector. MiDAS integrates multiple pre-trained and fine-tuned models with their training data to create a domain-invariant representation. Then, MiDAS uses local Lipschitz smoothness of the invariant embedding space to estimate each model's relevance to a new sample. Higher ranked models provide predictions, and lower ranked models abstain. We evaluate MiDAS on generalization to drifted data with 9 fake news datasets, each obtained from different domains and modalities. MiDAS achieves new state-of-the-art performance on multi-domain adaptation for out-of-distribution fake news classification.
