Analysis of Articles that Correct Other Posts on Social Media Aimed at Promoting the Experience in Examining Fakes

R. Onuma, H. Kaminaga, H. Nakayama, Y. Miyadera, Keito Suzuki, Shoichi Nakamura
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Abstract

Social media is increasingly being used as a tool to gather a wide variety of information. However, there are fake articles on social networking services mixed in with useful posts. It is desirable for users to use social networking services while determining the truth or falsity of articles. However, such judgement is difficult for inexperienced users since the skills to determine the authenticity of articles should be obtained by a stacking of experiences. In this research, we aim to develop methods for gaining experience with examining fake articles by suggesting noteworthy articles on the basis of an analysis of others’ responses to the articles. This paper describes methods for extracting articles that correct other posts on the basis of the characteristics of people’s responses to articles on social networking services and for extracting candidates for fake articles by analyzing such articles. Finally, we describe an experiment using a prototype system and discuss the effectiveness of our system as based on its results.
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社交媒体上纠正他人帖子的文章分析,旨在提升审假经验
社交媒体越来越多地被用作收集各种信息的工具。然而,在社交网络(sns)上,也有虚假文章混入有用的帖子。用户在判断文章的真假的同时使用社交网络服务是可取的。然而,对于没有经验的用户来说,这种判断是困难的,因为确定文章真实性的技能需要通过经验的积累来获得。在这项研究中,我们的目标是开发方法,通过在分析他人对文章的反应的基础上,提出值得注意的文章,从而获得检查假文章的经验。本文描述了基于社交网络服务上人们对文章的反应特征来提取纠正其他帖子的文章的方法,以及通过分析这些文章来提取假文章候选人的方法。最后,我们描述了一个使用原型系统的实验,并根据实验结果讨论了系统的有效性。
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