虚假新闻检测的多分类划分聚合框架

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wen Zhang;Haitao Fu;Huan Wang;Zhiguo Gong;Pan Zhou;Di Wang
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引用次数: 0

摘要

如今,随着人类活动向社交媒体转移,假新闻检测已经成为一个关键问题。现有的方法忽略了网络新闻的分类差异,不能充分利用多分类知识。例如,在处理“一只老鼠被一只猫吓坏了”的帖子时,学习“计算机”知识的模型往往会误解“老鼠”并给出假标签,而学习“动物”知识的模型往往会给出真标签。因此,本研究提出了一种多分类的分类聚合框架,命名为$CKA$,该框架创新性地在训练阶段学习分类知识,并在预测阶段对分类知识进行聚合。它由三个主要部分组成:新闻特征描述器、集成协调器和真相预测器。新闻特征器负责提取新闻特征,获得新闻分类。集成协调器与新闻特征器合作,在训练阶段生成分类特定模型,以最大限度地保留分类知识,其中每个分类特定模型在相应的新闻分类上最大化假新闻的检测性能。此外,为了在预测阶段对分类知识进行聚合,基于分类特定模型的可靠性评估,真值预测器使用真值发现技术对来自不同分类特定模型的预测进行聚合。大量实验证明,我们提出的$CKA$在假新闻检测方面优于最先进的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3A Multi-Classification Division-Aggregation Framework for Fake News Detection
Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post “A mouse is frightened by a cat,” a model that learns “computer” knowledge tends to misunderstand “mouse” and give a fake label, but a model that learns “animal” knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named $CKA$, which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed $CKA$ outperforms state-of-the-art baselines in fake news detection.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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