论文标题
无声：揭示新闻背后的意图
SirenLess: reveal the intention behind news
论文作者
论文摘要
如今，新闻文章往往越来越误导，阻止读者对某些事件做出主观判断。尽管已经提出了一些机器学习方法来检测误导新闻，但其中大多数是黑匣子，为人类在决策方面提供了有限的帮助。在本文中，我们提出了Sirenless，这是一种视觉分析系统，用于通过语言特征误导新闻检测。该系统具有文章Explorer，这是一种新颖的交互式工具，该工具将新闻元数据和语言特征集成在一起，以揭示新闻文章的语义结构并促进文本分析。我们使用Sirenless分析了来自不同来源的18篇新闻文章，并总结了一些有用的模式来误导新闻发现。对新闻专业人士和大学生进行了一项用户研究，以确认我们系统的有用性和有效性。
News articles tend to be increasingly misleading nowadays, preventing readers from making subjective judgments towards certain events. While some machine learning approaches have been proposed to detect misleading news, most of them are black boxes that provide limited help for humans in decision making. In this paper, we present SirenLess, a visual analytical system for misleading news detection by linguistic features. The system features article explorer, a novel interactive tool that integrates news metadata and linguistic features to reveal semantic structures of news articles and facilitate textual analysis. We use SirenLess to analyze 18 news articles from different sources and summarize some helpful patterns for misleading news detection. A user study with journalism professionals and university students is conducted to confirm the usefulness and effectiveness of our system.
