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  "fetched_at": "2026-10-19T17:11:43Z",
  "source_url": "https://www.kaggle.com/competitions/fakenewskdd2020/data",
  "pages": [
    {
      "name": "data-description",
      "content": "train.csv: A full training dataset with the following attributes:\n<ul>\n<li>text: text of the article</li>\n<li>label: a label that marks the article as potentially unreliable</li>\n<ul>\n<li>1: fake</li>\n<li>0: true</li>\n</ul>\n\ntest.csv: A test dataset with the following attributes:\n<ul>\n<li>id: the id of the article text</li>\n<li>text: text of the article</li>\n</ul>\n\n sample_submission.csv: A sample submission with the following attributes.\n<ul>\n<li>id: the id of the article text from the test data</li>\n<li>label: a label that marks the article as potentially unreliable</li>\n<ul>\n<li>1: fake</li>\n<li>0: true</li>\n</ul>",
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    {
      "name": "Description",
      "content": "We are hosting a fake news detection shared task for the **[Second International TrueFact Workshop: Making a Credible Web for Tomorrow](https://truthdiscoverykdd2020.github.io/)** in conjunction with **[SIGKDD 2020](https://www.kdd.org/kdd2020/)**.\n\nIn this shared task, the participants will design a system to distinguish fake claims from authentic ones. \n\n**Acknowledgements**\nWe thank *[Kai Shu](http://www.public.asu.edu/~skai2/)*, Arizona State University for providing this challenging dataset and co-organize this task.",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is accuracy given by:\n\n$$accuracy=\\frac{correct\\ predictions}{correct\\ predictions+incorrect\\ predictions}$$\n\nSubmission Format\nFor every article in the test dataset, submission files should contain two columns: `id` and `label`. The `id` column should refer to the id in the `test.csv` file, and the `label` column should refer it's class of true (`0`), or fake (`1`) article (refer to sample_submissions.csv format).\n",
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    {
      "name": "rules",
      "content": "\n<h3>One account per participant</h3>\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n<h3>No private sharing outside teams</h3>\n<p>\n    Privately sharing code or data outside of teams is not permitted.\n    It's okay to share code if made available to all participants on the forums.\n</p>\n<h3>Team Mergers</h3>\n<p>Team mergers are not allowed in this competition.</p>\n\n<h3>Team Limits</h3>\n<p>There is no maximum team size.</p>\n<h3>Submission Limits</h3>\n<p>You may submit a maximum of 2 entries per day.</p>\n<p>You may select up to 2 final submissions for judging.</p>\n<h3>Competition Timeline</h3>\n<p>Start Date: <strong>6/24/2020</strong></p>\n<p>End Date: <strong>8/7/2020</strong></p>\n<h3>Reproducibility</h3>\n<p>The winners need to release their codes and reproduce the winning submissions to qualify for the prize.</p>",
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    {
      "name": "Final Poster And Presentation",
      "content": "We invite all the teams to submit (i) a **poster** with 1-2 pages of content in ACM Proceedings Template (pdf format with name, affiliation and email-id) **and/or** (ii) 7 mins of **video presentation** (mp4 format) outlining the solution, method and experimental results.\n\nWe will host the content in our official workshop website. Please upload the files in google drive and share the drive link in the discussion thread by **8/23/2020**.",
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