假新闻
可靠性
计算机科学
推论
数据科学
集合(抽象数据类型)
社交网络(社会语言学)
社会化媒体
可信赖性
主题模型
社会网络分析
万维网
人工智能
互联网隐私
情报检索
政治学
程序设计语言
法学
作者
Jiawei Zhang,Limeng Cui,Yanjie Fu,Fisher B. Gouza
出处
期刊:Cornell University - arXiv
日期:2018-05-22
被引量:18
摘要
In recent years, due to the booming development of online social networks,
fake news for various commercial and political purposes 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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