一种用于点击诱饵检测的新型对比学习方法：基于罗马尼亚新闻文章点击诱饵语料库 RoCliCo
A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles
AI总结：
针对罗马尼亚语缺乏点击诱饵语料库的问题，构建了含8,313个样本的RoCliCo数据集，并提出一种基于BERT的对比学习模型，通过度量标题与内容的余弦相似度来检测点击诱饵，实验验证了其有效性。
摘要：
为了增加收入，新闻网站常常使用具有欺骗性的新闻标题，诱使用户点击标题并阅读完整新闻。点击诱饵检测旨在自动识别这种虚假广告形式，避免浪费在线用户的宝贵时间。尽管该任务十分重要，但据我们所知，目前尚没有公开可用的罗马尼亚语点击诱饵语料库。为此，我们引入了一个新的罗马尼亚点击诱饵语料库（RoCliCo），包含8,313个新闻样本，这些样本被人工标注为点击诱饵和非点击诱饵标签。此外，我们使用四种机器学习方法进行了实验，从手工特征模型到循环神经网络和基于Transformer的神经网络，以建立一组有竞争力的基线。我们还进行了加权投票集成的实验。在考虑的基线中，我们提出了一种新的基于BERT的对比学习模型，该模型学习将新闻标题和内容编码到深度度量空间中，使得非点击诱饵新闻的标题和内容具有高余弦相似度，而点击诱饵新闻的标题和内容具有低余弦相似度。我们的数据集和复现基线的代码可在https://github.com/dariabroscoteanu/RoCliCo 公开下载。
英文摘要：
To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this form of false advertisement and avoid wasting the precious time of online users. Despite the importance of the task, to the best of our knowledge, there is no publicly available clickbait corpus for the Romanian language. To this end, we introduce a novel Romanian Clickbait Corpus (RoCliCo) comprising 8,313 news samples which are manually annotated with clickbait and non-clickbait labels. Furthermore, we conduct experiments with four machine learning methods, ranging from handcrafted models to recurrent and transformer-based neural networks, to establish a line-up of competitive baselines. We also carry out experiments with a weighted voting ensemble. Among the considered baselines, we propose a novel BERT-based contrastive learning model that learns to encode news titles and contents into a deep metric space such that titles and contents of non-clickbait news have high cosine similarity, while titles and contents of clickbait news have low cosine similarity. Our data set and code to reproduce the baselines are publicly available for download at https://github.com/dariabroscoteanu/RoCliCo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
