停止点击诱饵：检测和预防在线新闻媒体中的点击诱饵
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
摘要：
大多数在线新闻媒体在很大程度上依赖于读者点击产生的收入，由于存在众多这样的媒体，它们需要相互竞争以吸引读者的注意力。为了吸引读者点击一篇文章并随后访问媒体网站，媒体往往会在文章链接中附上吸引人的标题，吸引读者点击链接。这样的标题被称为点击诱饵。虽然这些诱饵可能会诱使读者点击，但从长远来看，点击诱饵通常不会达到读者的期望，并让他们失望。在这项工作中，我们试图自动检测点击诱饵，然后建立一个浏览器扩展，警告读者的不同媒体网站的可能性，被这样的标题。该扩展还为每个读者提供了一个阻止她不想看到的点击诱饵的选项。然后，使用这样的读者选择，扩展程序自动阻止类似的点击诱饵在她未来的访问。我们在多个媒体网站上进行了广泛的离线和在线实验，发现所提出的点击诱饵检测和个性化拦截方法表现非常好，检测准确率达到93%，拦截点击诱饵的准确率达到89%。
英文摘要：
Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.
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。
