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首页> 外文会议>China National Conference on Computational Linguistics >Clickbait Detection with Style-Aware Title Modeling and Co-attention
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Clickbait Detection with Style-Aware Title Modeling and Co-attention

机译：单击具有样式感知标题建模和共识的检测

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摘要

Clickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms to improve the quality of web content and the satisfaction of users. Clickbait detection is typically formed as a binary classification task based on the title and body of a webpage, and existing methods are mainly based on the content of title and the relevance between title and body. However, these methods ignore the stylistic patterns of titles, which can provide important clues on identifying clickbaits. In addition, they do not consider the interactions between the contexts within title and body, which are very important for measuring their relevance for clickbait detection. In this paper, we propose a clickbait detection approach with style-aware title modeling and co-attention. Specifically, we use Transformers to learn content representations of title and body, and respectively compute two content-based clickbait scores for title and body based on their representations. In addition, we propose to use a character-level Transformer to learn a style-aware title representation by capturing the stylistic patterns of title, and we compute a title stylistic score based on this representation. Besides, we propose to use a co-attention network to model the relatedness between the contexts within title and body, and further enhance their representations by encoding the interaction information. We compute a title-body matching score based on the representations of title and body enhanced by their interactions. The final clickbait score is predicted by a weighted summation of the aforementioned four kinds of scores. Extensive experiments on two benchmark datasets show that our approach can effectively improve the performance of clickbait detection and consistently outperform many baseline methods.
机译：ClickBait是一种Web内容的形式，旨在吸引注意力并诱使用户单击特定的超链接。 ClickBaits的检测是在线平台提高网络内容质量和用户满意度的重要任务。 ClickBait检测通常基于网页的标题和正文形成为二进制分类任务，并且现有方法主要基于标题的内容和标题与身体之间的相关性。然而，这些方法忽略了标题的风格模式，这可以提供识别ClickBATITS的重要线索。此外，他们不考虑标题和机构内的上下文之间的相互作用，这对于测量其对ClickBit检测的相关性非常重要。在本文中，我们提出了一种ClickBit检测方法，具有风格感知标题建模和共同关注。具体地，我们使用变压器学习标题和正文的内容表示，并分别根据其表示来分别计算标题和正文的基于内容的ClickBit分数。此外，我们建议通过捕获标题的风格模式来学习风格型模式，从而使用字符级变压器来学习样式感知标题表示，并且我们基于此表示来计算标题风格分数。此外，我们建议使用共同关注网络来模拟标题和身体内的上下文之间的相关性，并通过编码交互信息进一步增强它们的表示。我们根据其互动增强的标题和身体的表示计算标题身体匹配分数。最终的ClickBait得分是通过上述四种分数的加权求和来预测的。两个基准数据集的广泛实验表明，我们的方法可以有效地提高ClickBit检测的性能，并始终如一地优于许多基线方法。

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