Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.
翻译:今天的在线用户每天都会受到误导和宣传性新闻文章和媒体文章的影响,因此,为了对付这种情况,设计了一些办法,以实现更健康、更安全的在线新闻和媒体消费。自动系统能够支持人类检测此类内容;然而,阻碍其广泛采用的一个主要障碍是,除了准确外,这些系统的决定也需要解释,才能被用户信任和广泛采纳。由于误导和宣传性内容通过使用一些欺骗技术影响读者,我们提议检测和展示使用这类技术作为提供解释性的方法。特别是,我们界定定性描述特征,分析其是否适合检测欺骗技术。我们进一步表明,我们可解释的特征很容易与经过培训的语言模式相结合,产生最新的结果。