Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks.
One approach that has recently gained attention detects these fake news using stylometry-based provenance, i. e. tracing a text's writing style back to its producing source and determining whether the source is malicious.
In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively.
As the detection of fake news is increasingly considered a technological problem, it has attracted considerable research.
One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim.
Popular fake news articles spread faster than mainstream articles on the same topic which renders manual fact checking inefficient.
This paper surveys and presents recent academic work carried out within the field of stance classification and fake news detection.
News in social media such as Twitter has been generated in high volume and speed.
While the correlations among news articles have been shown to be effective cues for online news analysis, existing deep-learning-based methods often ignore this information and only consider each news article individually.
Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.