Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset
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/.
Code (0)
등록된 구현이 없습니다.
Tasks
Fake News DetectionSimilar Papers 제목 키워드 기반
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset
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. Contempor…
Fake News DetectionAnnotating the Focus of Negation in Japanese Text
This paper proposes an annotation scheme for the focus of negation in Japanese text. Negation has its scope and the focus within the scope. The scope of negation is the part of the sentence that is negated; the focus is …
Natural Language InferenceNegationOpinion MiningQuestion Answering+1FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typically focus solely on news pertaining t…
Domain AdaptationFake News DetectionFASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis
In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the absence of annotated corpora, hindering its effective processing, notably in Machine Learning (ML) applications reliant on c…
Fake News DetectionSentiment AnalysisBootstrapping Multi-view Representations for Fake News Detection
Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fid…
Decision MakingFake News Detection