Abstract
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.
Abstract (translated)
在线操纵新闻是一个日益增长的问题,需要使用自动化系统来控制其传播。我们认为,尽管关于虚假信息和误导性的检测已经在进行研究,但在新闻文章中发现有害议程的重要开放挑战方面缺乏投资;识别有害议程是标记最具实际危害的新闻运动的关键。此外,由于对审查的真实担忧,有害的议程检测必须可解释,才能有效。在这个工作中,我们提出了这个新的任务,并发布了一个注释性新闻文章的元数据集,名为NewsAgendas,以用于议程识别。我们展示了可解释的系统在这项任务中的有效作用,并证明了它们与黑盒模型的性能和表现相当。
URL
https://arxiv.org/abs/2302.00102
