You have available the list of tweet IDs and a script that you can use to download the tweets. The script downloads the raw HTML data, you will still need to extract the tweet information from the downloaded data.
\r\nCopyright restrictions from Twitter does not allow us to distribute the contents of the tweets, that's why we are providing the IDs and a script to download the tweets. The script incorporates a \"sleep\" instruction to avoid sending all requests at once, so allow several hours to download the tweets. Alternatively you may want to register to Twitter Developers and use their API to download the tweets.
\r\nThere is a training set and a test set. The training set contains 2000 tweets sorted in time, together with the location mentions. The format of this file is exactly the same as the format of the submission file. The test set contains just over 1000 tweets sorted in time, this time without the location mentions. Your task is to find the location mentions of this test set and submit the results to Kaggle in Class. The timestamp of the tweets of the test set are after those of the training set, to model a realistic scenario where we train on known tweets and we want to predict the location mentions in future tweets.
\r\nFile descriptions
\r\nPlease read the task description at the ALTA 2014 shared task website.
\r\nThis task is a reprise of the original ALTA 2014 shared task, but made open to the public on an ongoing basis.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:
\r\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]
\r\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
\r\nFor every tweet in the dataset, submission files should contain two columns: TweetId and LocationMentions. LocationMentions should be a space-delimited list. If a location mention is repeated, number each repeated location. If a location has multiple words, separate them with blank space so that, in effect, it does not matter whether it is one location expression with two words or two different location expressions. Thus, if a tweet with ID \"1234\" has two location expressions \"London\" and \"United States\" the following are valid and equivalent descriptions:
\r\n1234,london united states\r\n
1234,united london states
The file should contain a header and have the following format:
\r\nTweetId,LocationMentions", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n
235423423559754,uk australia1 london australia2
493450763931512832,france germany us uk libya
etc.
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\nTeam mergers are not allowed in this competition.
\r\n\r\nThere is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 7/7/2015 1:03 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 12/31/2017 11:59 PM UTC
\r\n\t\r\nPlease be aware that you must not redistribute Tweets, per the Twitter usage agreement.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }