ترجمه مقاله در زمینه هوش مصنوعی

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ABSTRACT

In recent years, due to the booming development of online social

networks, fake news for various commercial and political pur

has been appearing in large numbers and widespread in the online

world. With deceptive words, online social network users can get

infected by these online fake news easily, which has brought about

tremendous effects on the offline society already. An important

goal in improving the trustworthiness of information in online

social networks is to identify the fake news timely. This paper

aims at investigating the principles, methodologies and algorithms

for detecting fake news articles, creators and subjects from online

social networks and evaluating the corresponding performance.

This paper addresses the challenges introduced by the unknown

characteristics of fake news and diverse connections among news

articles, creators and subjects. Based on a detailed data analysis, this

paper introduces a novel automatic fake news credibility inference

model, namely FakeDetector. Based on a set of explicit and latent

features extracted from the textual information, FakeDetector

builds a deep diffusive network model to learn the representations

of news articles, creators and subjects simultaneously. Extensive

experiments have been done on a real-world fake news dataset to

compare FakeDetector with several state-of-the-art models, and

the experimental results have demonstrated the effectiveness of the

proposed model.

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