结合信息发散度量和深度学习技术的谣言和标题党检测

Christian Oliva, Ignacio Palacio Marín, L. F. Lago-Fernández, David Arroyo
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引用次数: 1

摘要

在这篇文章中,我们解决了在点击诱饵的特定场景中检测误导性信息的产生和传播的挑战。我们的贡献包括一种结合了深度神经网络和信息发散度量的方法,以克服这种情况下深度学习技术的局限性。这个分析是通过考虑一个标题党挑战数据集来进行的。我们意识到,用于研究这类问题的数据集的构造极大地影响了模型的性能,从而影响了它的选择。由于标题党是标题和内容不一致的结果,我们将发散度量作为深度学习模型的一层。由此产生的模型克服了传统机器学习和深度学习模型在标题党检测中的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rumor and clickbait detection by combining information divergence measures and deep learning techniques
In this article we address the challenge of detecting the generation and spreading of misleading information in the specific scenario of clickbait. Our contribution consists of a methodology that combines a deep neural network and an information divergence measure to overcome the limitations of deep learning techniques in this scenario. This analysis is conducted by considering a clickbait challenge dataset. We realise that the construction of the dataset used to study this kind of problems dramatically affects the performance of the model and, thus, its selection. Since clickbait is a result of the inconsistency between headlines and content, we integrate a divergence measure as a layer of a deep learning model. The resulting model overcomes the limitations of conventional machine learning and deep learning models in clickbait detection.
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