You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\n\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
\nTeam mergers are not allowed in this competition.
\n\nEach team can consist of a maximum of 3 team members.
\nYou may submit a maximum of 2 entries per day.
\nYou may select up to 2 final submissions for judging.
\nStart Date: TBA
\nMerger Deadline: None
\nEntry Deadline: None
\nEnd Date: 12/13/2018 12:00 AM UTC
\n\n\t\n\nThe task is open to the public. Individuals, institutions of higher education, research institutes, enterprises, or other organizations can all sign up for the task.
\nPersonnel of the WSDM Cup 2019 organization team are not allowed to participate in the task;
\nThe final interpretation right belongs to the organizing committee of the WSDM Cup 2019.\n\nThe use of external data is only allowed for training of the word segmenters and embedding models, and the external data is required to share with the Sponsor and Kaggle afterward. Other than the above mentioned tools, the models submitted to the task should be trained solely with the training data provided by the Sponsor and Kaggle.
\n\n\nParticipants who win the Championship, Runner-up or 3rd-place are required to submit a technical report to The 12th ACM International WSDM Conference.
\nYour code must be submitted with your paper and should be able to reproduce the result on the leaderboard.
\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "We invite researchers and students in the community to take part in the following task:\n\nTask: Fake News Classification\n\nGiven the title of a fake news article A and the title of a coming news article B, participants are asked to classify B into one of the three categories.\n\n- agreed: B talks about the same fake news as A\n- disagreed: B refutes the fake news in A\n- unrelated: B is unrelated to A\n\n## File \n- **train.csv** - training data contains 320,767 news pairs in both Chinese and English. This file provides the only data you can use to finish the task. Using external data is not allowed.\n- **test.csv** - testing data contains 80,126 news pairs in both Chinese and English. The approximately 25% of the testing data is set to be public and is used to calculate your accuracy shown on the leading board. The remaining 75% private data is used to calculate your final result of the competition.\n- **sample_submission.csv** - sample answer to the testing data.\n\n\n## Data fields\n- **id** - the id of each news pair.\n- **tid1** - the id of fake news title 1.\n- **tid2** - the id of news title 2.\n- **title1_zh** - the fake news title 1 in Chinese.\n- **title2_zh** - the news title 2 in Chinese.\n- **title1_en** - the fake news title 1 in English.\n- **title2_en** - the news title 2 in English.\n- **label** - indicates the relation between the news pair: agreed/disagreed/unrelated.\n\nThe English titles are machine translated from the related Chinese titles. This may help participants from all background to get better understanding of the datasets. Participants are highly recommended to use the Chinese version titles to finish the task. ", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "### [06-Jan-2019 Update] Final Result Announcement\nWe congratulate the following top three teams for winning the competition:\n\n1. IM\n2. Travel\n3. IKM Lab\n\n### Background\n\nWSDM (pronounced \"wisdom\") is one of the premier conferences on web-inspired research involving search and data mining. [The 12th ACM International WSDM Conference][1] will take place in Melbourne, Australia during Feb. 11-15, 2019. \n\nThis task is organized by ByteDance, the Platinum Level Sponsor of the conference. ByteDance is a global Internet technology company started from China. Our goal is to build a global content platform that enable people to enjoy various content in various forms. We inform, entertain, and inspire people across language, culture and geography.\n\nOne of the challenges which we are facing is to combat different types of fake news. Fake news here refers to all forms of false, inaccurate or misleading information, which now poses a big threat to human civilization.\n\nAt Bytedance, we have created a large-scale database to store existing fake news articles. Any new article must go through a test on the truthfulness of content before being published. We conduct matching between the new article and the articles in the database. Articles identified as containing fake news will be withdrawn after human verification. The accuracy and efficiency of the process, therefore, becomes crucial for us to make the platform safe, reliable, and healthy.\n\n### About the Task\n\nTask: classification of news article\n\nGiven the title of a fake news article A and the title of a coming news article B, participants are asked to classify B into one of the three categories.\n\n- agreed: B talks about the same fake news as A\n\n- disagreed: B refutes the fake news in A\n\n- unrelated: B is unrelated to A\n\n\n### Prize\n- **Champion**: One team ($AUD2000) + One free conference registration \n- **Runner-up**: One team ($AUD750) + One free conference registration\n- **3rd-place**: One team ($AUD250) + One free conference registration\n\n\n\n [1]: http://www.wsdm-conference.org/2019/", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "### Evaluation \n\nWe use **Weighted Categorization Accuracy** to evaluate your performance. Weighted categorization accuracy can be generally defined as:\n\n$$ WeightedAccuracy(y, \\hat{y}, \\omega) = \\frac{1}{n} \\displaystyle{\\sum_{i=1}^{n}} \\frac{\\omega_i(y_i=\\hat{y}_i)}{\\sum \\omega_i} $$\n\nwhere \\\\(y\\\\) are ground truths, \\\\(\\hat{y}\\\\) are the predicted results, and \\\\(\\omega_i\\\\) is the weight associated with the \\\\(i\\\\)th item in the dataset.\n\nIn our test set, we assign each testing item a weight according to its category. The weights of the three categories, agreed, disagreed and unrelated are \\\\(\\frac{1}{15}\\\\), \\\\(\\frac{1}{5}\\\\), \\\\(\\frac{1}{16}\\\\), respectively. We set the weights in consideration of the imbalance of the data distribution to minimize the bias to your performance caused by the majority class (unrelated pairs accounts for approximately 70% of the dataset). ", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }