Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
オンラインコンテンツの発行者は、ユーザーを自分たちのウェブサイトに誘引するために、記事に目を引く見出しをよく使います。これらの見出しは一般に「クリックベイト」として知られており、ユーザーの好奇心の隙を利用して、しばしば彼らを失望させるリンクをクリックするよう誘導します。クリックベイトを自動的に検出する既存の方法は、高度な特徴エンジニアリングとドメイン知識に依存しています。
データセット
train1.csvは、Abhijnan Chakraborty、Bhargavi Paranjape、Sourya Kakarla、およびNiloy Gangulyから収集されました。「Stop Clickbait: Detecting and Preventing Click baits in Online News Media」（オンラインニュースメディアにおけるクリックベイトの検出と防止）。2016年8月、米国サンフランシスコで開催された2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)の会議録に掲載。[GitHub](https://github.com/bhargaviparanjape/clickbait/tree/master/dataset)
このデータセットには2つの列があり、最初の列には見出しが含まれ、2番目の列にはクリックベイトの数値ラベルがあり、1はクリックベイトであることを表し、0は非クリックベイトの見出しであることを表します。データセットには合計32,000行が含まれており、そのうち50％がクリックベイトで、残りの50％が非クリックベイトです。
train2.csvは、Kaggle InClass Prediction Competitionの[Clickbait news detection dataset](https://www.kaggle.com/c/clickbait-news-detection/data)から収集されました。このデータセットにはニュースのタイトル、本文、およびラベルが含まれています。
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- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
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