In digital environments where substantial amounts of information are shared online, news headlines play essential roles in the selection and diffusion of news articles. Some news articles attract audience attention by showing exaggerated or misleading headlines. This study addresses the \textit{headline incongruity} problem, in which a news headline makes claims that are either unrelated or opposite to the contents of the corresponding article. We present \textit{BaitWatcher}, which is a lightweight web interface that guides readers in estimating the likelihood of incongruence in news articles before clicking on the headlines. BaitWatcher utilizes a hierarchical recurrent encoder that efficiently learns complex textual representations of a news headline and its associated body text. For training the model, we construct a million scale dataset of news articles, which we also release for broader research use. Based on the results of a focus group interview, we discuss the importance of developing an interpretable AI agent for the design of a better interface for mitigating the effects of online misinformation.

中文翻译：

---

BaitWatcher：用于检测不一致新闻标题的轻量级 Web 界面

在大量信息在线共享的数字环境中，新闻标题在新闻文章的选择和传播中起着至关重要的作用。一些新闻文章通过显示夸张或误导性的标题来吸引观众的注意力。这项研究解决了 \textit{headline incongruity} 问题，其中新闻标题提出与相应文章的内容无关或相反的声明。我们展示了 \textit{BaitWatcher}，这是一个轻量级的网络界面，可指导读者在点击标题之前估计新闻文章中不一致的可能性。BaitWatcher 利用分层循环编码器有效地学习新闻标题及其相关正文的复杂文本表示。为了训练模型，我们构建了百万规模的新闻文章数据集，我们也将其发布用于更广泛的研究用途。根据焦点小组访谈的结果，我们讨论了开发可解释的 AI 代理以设计更好的界面以减轻在线错误信息影响的重要性。