A Domain-Independent Holistic Approach to Deception Detection
A Domain-Independent Holistic Approach to Deception Detection
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DOI：
10.26615/978-954-452-072-4_147
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Sadat Shahriar;Arjun Mukherjee;O. Gnawali
中科院分区：
文献类型：
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作者：
Sadat Shahriar;Arjun Mukherjee;O. Gnawali
文本中的欺骗在不同的领域可以有不同的形式，包括假新闻，谣言推文和垃圾邮件。无论在哪个领域，欺骗性文本的主要意图都是欺骗读者。虽然存在特定于域的欺骗检测，但与域无关的欺骗检测可以提供一个整体的画面，这对于理解文本中的欺骗是如何发生的至关重要。在本文中，我们使用深度学习架构在独立于域的环境中检测欺骗。我们的方法优于大多数基准数据集的最先进性能，总体准确率为93.42%，F1得分为93.22%。领域独立训练使我们能够捕捉到欺骗性写作风格的细微差别。此外，我们分析了有多少域内数据可能有助于准确检测欺骗，特别是对于数据可能不容易用于训练的情况。我们的研究结果和分析表明，可能有一个普遍的欺骗模式躺在之间的文本独立的域，这可以创建一个新的研究领域，开辟了新的途径，在欺骗检测领域。
The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the deceptive text is to deceit the reader. Although domain-specific deception detection exists, domain-independent deception detection can provide a holistic picture, which can be crucial to understand how deception occurs in the text. In this paper, we detect deception in a domain-independent setting using deep learning architectures. Our method outperforms the State-of-the-Art performance of most benchmark datasets with an overall accuracy of 93.42% and F1-Score of 93.22%. The domain-independent training allows us to capture subtler nuances of deceptive writing style. Furthermore, we analyze how much in-domain data may be helpful to accurately detect deception, especially for the cases where data may not be readily available to train. Our results and analysis indicate that there may be a universal pattern of deception lying in-between the text independent of the domain, which can create a novel area of research and open up new avenues in the field of deception detection.