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  "fetched_at": "2026-10-19T08:44:34Z",
  "source_url": "https://www.kaggle.com/competitions/datalabcup1-predicting-news-popularity/data",
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      "name": "data-description",
      "content": "## File descriptions\n\n*   **train.csv** - the training set (contains the label - Popularity)\n*   **test.csv** - the test set (you must predict the label - Popularity)\n*   **sample_submission.csv** - the sample of submission format\n\n## Data fields\n\n*   **Id** - non-negative integer assigned uniquely to each news\n*   **Page content** - the raw web page of news\n*   **Popularity** - the popularity of news",
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      "name": "Description",
      "content": "In this competition, you are provided with a supervised dataset X consisting of the raw content of news articles and the binary popularity (where 1 means \"popular\" and -1 not, calculated based on the number of shares in online social networking services) of these articles as labels. Your goal is to learn a function f from X that is able to predict the popularity of an unseen news article.\n\nThis is the first competition for course CS565500 at NTHU in Taiwan.",
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      "name": "Evaluation",
      "content": "The evaluation metric is Area Under ROC. You can calculate AUC by sklearn.metrics:\n\n```\nfrom sklearn.metrics import roc_auc_score\nauc = roc_auc_score(y_true, y_pred)\n```\n\nThe ranking shown on the leaderboard before the end of competition reflects only the AUC performance over part of test.csv. However, this is not how we evaluate your final scores. After the competition, we calculate AUC over the entire test.csv and report the final ranking thereby.\n\nThere will be two baseline results, namely, Benchmark-60 and Benchmark-80. You have to outperform Benchmark-60 to get 60 points, and Benchmark-80 to get 80. Meanwhile, the higher AUC you achieve, the higher the final score you will get.\n\n## Submission Format\n\nFor every news in the test.csv, submission files should contain two columns: Id and Popularity. The field Popularity is double value between [0,1] indicating the probability of news being popular.\n\n```\nId Popularity\n0    0.9\n1    0.4\n2    0.7\n3    0.1\n```",
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      "name": "rules",
      "content": "## Honor Code\nPlease review the Honor Code prior to accepting the rules.\n\n## One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n## Team Mergers\nTeam mergers are not allowed in this competition.\n\n## Team Limits\nThe maximum size of a team is 4 participants.",
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