  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.
