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Fake news provokes many societal problems; therefore, there has beenextensive research on fake news detection tasks to counter it. Many fake newsdatasets were constructed as resources to facilitate this task. Contemporaryresearch focuses almost exclusively on the factuality aspect of the news.However, this aspect alone is insufficient to explain "fake news," which is acomplex phenomenon that involves a wide range of issues. To fully understandthe nature of each instance of fake news, it is important to observe it fromvarious perspectives, such as the intention of the false news disseminator, theharmfulness of the news to our society, and the target of the news. We proposea novel annotation scheme with fine-grained labeling based on detailedinvestigations of existing fake news datasets to capture these various aspectsof fake news. Using the annotation scheme, we construct and publish the firstJapanese fake news dataset. The annotation scheme is expected to provide anin-depth understanding of fake news. We plan to build datasets for bothJapanese and other languages using our scheme. Our Japanese dataset ispublished at https://hkefka385.github.io/dataset/fakenews-japanese/.

Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset