Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.

翻译：在线操控新闻是一个日益严峻的问题，亟需自动化系统来遏制其传播。我们认为，尽管虚假信息与错误信息检测已有研究，但在识别新闻文章中有害议程这一关键开放性挑战上，投入仍显不足；识别有害议程对于标记可能造成最大现实危害的新闻运动至关重要。此外，由于对审查制度的真实关切，有害议程检测器必须具备可解释性才能发挥效用。在本研究中，我们提出这一新任务，并发布一个名为NewsAgendas的标注新闻文章数据集，用于议程识别。我们展示了可解释系统在此任务上的有效性，并证明其性能可与黑箱模型相媲美。