  The blooming of fake news on social networks has devastating impacts on
society, economy, and public security. Although numerous studies are conducted
for the automatic detection of fake news, the majority tend to utilize deep
neural networks to learn event-specific features for superior detection
performance on specific datasets. However, the trained models heavily rely on
the training datasets and are infeasible to apply to upcoming events due to the
discrepancy between event distributions. Inspired by domain adaptation
theories, we propose an end-to-end adversarial adaptation network, dubbed as
MetaDetector, to transfer meta knowledge (event-shared features) between
different events. Specifically, MetaDetector pushes the feature extractor and
event discriminator to eliminate event-specific features and preserve required
event-shared features by adversarial training. Furthermore, the pseudo-event
discriminator is utilized to evaluate the importance of historical event posts
to obtain partial shared features that are discriminative for detecting fake
news. Under the coordinated optimization among the four submodules,
MetaDetector accurately transfers the meta-knowledge of historical events to
the upcoming event for fact checking. We conduct extensive experiments on two
large-scale datasets collected from Weibo and Twitter. The experimental results
demonstrate that MetaDetector outperforms the state-of-the-art methods,
especially when the distribution shift between events is significant.
Furthermore, we find that MetaDetector is able to learn the event-shared
features, and alleviate the negative transfer caused by the large distribution
shift between events.
