Abstract
News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.
Highlights
Fake information appears in a variety of forms, including videos, audio, images, and text.fake information in text form can be classified as news, social network services, speeches, documents, and so on
This study proposes a model for fake news detection by focusing on text-based fake news
We propose BAKE, an automatic fake news detection model that improves upon Bidirectional Encoder Representations from Transformers model (BERT) by mitigating the data imbalance problem
Summary
Fake information in text form can be classified as news, social network services, speeches, documents, and so on. This study proposes a model for fake news detection by focusing on text-based fake news. Various efforts have been undertaken to prevent the spread of fake news by providing a code of principles that fact check organizations around the world can use. Politifact (https://www.politifact.com) and snopes (https://www.snopes.com) developed a Fake news detection tool to classify the level of fake news in stages based on the presented criteria. These tools are time-consuming and expensive as they require manual work and judgment. A model that automatically detects fake news is required
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