使用揭穿推文的通用假新闻收集系统
Universal Fake News Collection System using Debunking Tweets
摘要：
大量的人使用社交网络服务（SNS）来方便地获取各种新闻，但他们有更多的机会获得和分享携带虚假信息的“假新闻”。部分为了打击假新闻，一些事实核查网站如Snopes和PolitiFact已经成立。然而，这些网站依赖于耗时和劳动密集型的任务。此外，它们可用的语文并不广泛。为了解决这些困难，我们提出了一个新的基于规则的（无监督）框架的假新闻收集系统，可以很容易地扩展到各种语言。该系统通过揭穿用户的推文来收集高概率的假新闻，并呈现聚集更高关注度的事件集群。我们的系统目前支持两种语言：英语和日语。它显示了事件集群，其中65%实际上是假的。在未来的研究中，它将应用于其他语言，并将与大型假新闻数据集一起发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
