SirenLess: reveal the intention behind news
This work addresses the issue of misleading news for readers and professionals, but it is incremental as it builds on existing detection methods with a focus on visualization.
The authors tackled the problem of misleading news by developing SirenLess, a visual analytical system that uses linguistic features to reveal semantic structures in news articles, and they validated its usefulness through a user study with journalism professionals and students.
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.