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LPHEADA is a multicountry and fully Labelled digital Public HEAlth DAtaset of tweets originated in Australia, Canada, UK, or US between November 28th, 2018 to June 19th, 2020. This dataset contains 366,405 crowd-generated labels (three labels per tweet) for 122,135 PASS-related tweets, labelled by 708 unique annotators on Amazon Mechanical Turk.

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data-intelligence-for-health-lab/Lpheada-Labelled-Public-HEAlth-DAtaset

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Lpheada- Labelled Public HEAlth DAtaset

DOI

This repository contains three six labelled datasets on digital public health surveillance.

How to Access the Data

To retrive a complete tweet object including text, data, user information, and location you will need to apply for a developer account to access Twitter APIs.

After creating the account, install twarc, an API to hydrate tweetr data from TweetIDs.

pip install twarc

To configure your twart requests, run the following script and enter the four credentials explained earlier.

twarc configure

Now you are ready to pass the files in the IDs folder to Twitter API and collect all the metadata associated with each ID.

Rehydrate the Dataset using TweetIDs or UersIDs

To rehydrate the dataset, you can use Twarc’s hydrate command can be used to rehydrate the full dataset using unique tweet identifiers. The output will be saves as a json file. Please use the Tweet_IDs folder for this purpose.

twarc hydrate PhysicalActivity-TweetIDs-Canada.txt > Canada_PA.jsonl

To only retrieve user's information (metadata), use Twarc's user command:

twarc users UserIDs.txt > user_meta.jsonl

Example


Geospatial Data Inference

To extract the location data, we use the {place} and {full place} fields of the Twitter dataset. For each country, we need a metadata of the geographical locations to map these fields to actual city/province/state names.

To infer the location data associated with each tweet, in addition to the place and full.place fields, we use user's profile information as well as the tweet text. The example provided in the above figure illustrated the overal process of this task. Please refer to LocationInference.ipynb for the script.

Citation

The manuscript that presents this dataset has been accepted for publication at JMIR Public Health and Surveillance. Please cite our paper if you use this dataset in your project.

@article{abad2022physical,
  title={Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research},
  author={Abad, Zahra Shakeri Hossein and Butler, Gregory P and Thompson, Wendy and Lee, Joon and others},
  journal={JMIR Public Health and Surveillance},
  volume={8},
  number={2},
  pages={e32355},
  year={2022},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

More Questions

Please use issues on this Github for any questions or feedback. You can also contact us at dih[at]ucalgary.ca or joonwu.lee[at]ucalgary.ca for specific inquiries.

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LPHEADA is a multicountry and fully Labelled digital Public HEAlth DAtaset of tweets originated in Australia, Canada, UK, or US between November 28th, 2018 to June 19th, 2020. This dataset contains 366,405 crowd-generated labels (three labels per tweet) for 122,135 PASS-related tweets, labelled by 708 unique annotators on Amazon Mechanical Turk.

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