With the rapid evolution of social media, fake news has become a significant
social problem, which cannot be addressed in a timely manner using manual
investigation. This has motivated numerous studies on automating fake news
detection. Most studies explore supervised training models with different
modalities (e.g., text, images, and propagation networks) of news records to
identify fake news. However, the performance of such techniques generally drops
if news records are coming from different domains (e.g., politics,
entertainment), especially for domains that are unseen or rarely-seen during
training. As motivation, we empirically show that news records from different
domains have significantly different word usage and propagation patterns.
Furthermore, due to the sheer volume of unlabelled news records, it is
challenging to select news records for manual labelling so that the
domain-coverage of the labelled dataset is maximized. Hence, this work: (1)
proposes a novel framework that jointly preserves domain-specific and
cross-domain knowledge in news records to detect fake news from different
domains; and (2) introduces an unsupervised technique to select a set of
unlabelled informative news records for manual labelling, which can be
ultimately used to train a fake news detection model that performs well for
many domains while minimizing the labelling cost. Our experiments show that the
integration of the proposed fake news model and the selective annotation
approach achieves state-of-the-art performance for cross-domain news datasets,
while yielding notable improvements for rarely-appearing domains in news
datasets.

本研究介绍了一种新的自动检测跨领域新闻中虚假消息的技术，该技术整合了领域特定和跨领域知识，并利用一种无监督的选择性注释技术来减少标注成本，并在跨领域的新闻数据集上实现了最先进的性能。

跨域多模态数据在假新闻检测中应用：接纳领域差异

Embracing Domain Differences in Fake News: Cross-domain Fake News  Detection using Multi-modal Data

A large part of the current success of deep learning lies in the
effectiveness of data -- more precisely: labelled data. Yet, labelling a
dataset with human annotation continues to carry high costs, especially for
videos. While in the image domain, recent methods have allowed to generate
meaningful (pseudo-) labels for unlabelled datasets without supervision, this
development is missing for the video domain where learning feature
representations is the current focus. In this work, we a) show that
unsupervised labelling of a video dataset does not come for free from strong
feature encoders and b) propose a novel clustering method that allows
pseudo-labelling of a video dataset without any human annotations, by
leveraging the natural correspondence between the audio and visual modalities.
An extensive analysis shows that the resulting clusters have high semantic
overlap to ground truth human labels. We further introduce the first
benchmarking results on unsupervised labelling of common video datasets
Kinetics, Kinetics-Sound, VGG-Sound and AVE.

本文提出了一种基于音频和视觉的聚类方法，可以实现对视频数据集的无监督标记。经过广泛的分析，结果聚类与人工标签有很高的语义重叠性。同时，该方法也为 Kinetics，Kinetics-Sound，VGG-Sound 和 AVE 等常见视频数据集的无监督标记带来了首批基准结果。