Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset
This work addresses the need for more comprehensive fake news datasets, particularly for Japanese language research, though it is incremental in extending annotation schemes to new languages.
The authors tackled the problem of fake news detection by proposing a novel annotation scheme with fine-grained labeling to capture multiple aspects beyond factuality, such as intention and harmfulness, and constructed the first Japanese fake news dataset to facilitate research.
Fake news provokes many societal problems; therefore, there has been extensive research on fake news detection tasks to counter it. Many fake news datasets were constructed as resources to facilitate this task. Contemporary research focuses almost exclusively on the factuality aspect of the news. However, this aspect alone is insufficient to explain "fake news," which is a complex phenomenon that involves a wide range of issues. To fully understand the nature of each instance of fake news, it is important to observe it from various perspectives, such as the intention of the false news disseminator, the harmfulness of the news to our society, and the target of the news. We propose a novel annotation scheme with fine-grained labeling based on detailed investigations of existing fake news datasets to capture these various aspects of fake news. Using the annotation scheme, we construct and publish the first Japanese fake news dataset. The annotation scheme is expected to provide an in-depth understanding of fake news. We plan to build datasets for both Japanese and other languages using our scheme. Our Japanese dataset is published at https://hkefka385.github.io/dataset/fakenews-japanese/.