论文标题
使用揭穿推文的通用假新闻收集系统
Universal Fake News Collection System using Debunking Tweets
论文作者
论文摘要
大量的人使用社交网络服务（SNS）来容易访问各种新闻，但是他们有更多的机会获得和分享``假新闻''，并带有虚假信息。部分是为了打击虚假新闻，已经建立了一些事实检查网站，例如Snopes和Politifact。然而，这些网站依靠耗时和劳动密集型的任务。此外，他们的可用语言并不广泛。为了解决这些困难，我们提出了一种基于规则（无监督）框架的新的假新闻收集系统，可以轻松扩展各种语言。该系统通过揭露用户的推文，并提出事件群集吸引了更高的关注，从而收集新闻。我们的系统目前用两种语言起作用：英语和日语。它显示了事件簇，其中65％实际上是假的。在以后的研究中，它将应用于其他语言，并将使用大型假新闻数据集发布。
Large numbers of people use Social Networking Services (SNS) for easy access to various news, but they have more opportunities to obtain and share ``fake news'' carrying false information. Partially to combat fake news, several fact-checking sites such as Snopes and PolitiFact have been founded. Nevertheless, these sites rely on time-consuming and labor-intensive tasks. Moreover, their available languages are not extensive. To address these difficulties, we propose a new fake news collection system based on rule-based (unsupervised) frameworks that can be extended easily for various languages. The system collects news with high probability of being fake by debunking tweets by users and presents event clusters gathering higher attention. Our system currently functions in two languages: English and Japanese. It shows event clusters, 65\% of which are actually fake. In future studies, it will be applied to other languages and will be published with a large fake news dataset.
