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  1. Z

    PAN16 Author Profiling

    • data.niaid.nih.gov
    • zenodo.org
    Updated Apr 9, 2020
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    Daelemans, Walter (2020). PAN16 Author Profiling [Dataset]. https://data.niaid.nih.gov/resources?id=ZENODO_3745962
    Explore at:
    Dataset updated
    Apr 9, 2020
    Dataset provided by
    Stein, Benno
    Rangel, Francisco
    Rosso, Paolo
    Potthast, Martin
    Verhoeven, Ben
    Daelemans, Walter
    Description

    We provide a training data set that consists of Twitter tweets in English, Spanish and Dutch.

    The English and Spanish datasets are labeled with age and gender, whereas the Dutch one only with gender. With regard to age, we will consider the following classes: 18-24, 25-34, 35-49, 50-64, 65-xx.

    Remark. Due to Twitter's privacy policy we cannot provide tweets directly, but only URLs referring to them. You will have to download them yourself. For your convenience, we provide a download software for this. We expect participants to extract gender and age information only from the textual part of a tweet and to discard any other meta information that may be provided by Twitter's API. When we evaluate your software at our site, we do not expect it downloads tweets. We will do this beforehand.

    More information about the task: Link

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Daelemans, Walter (2020). PAN16 Author Profiling [Dataset]. https://data.niaid.nih.gov/resources?id=ZENODO_3745962

PAN16 Author Profiling

Explore at:
Dataset updated
Apr 9, 2020
Dataset provided by
Stein, Benno
Rangel, Francisco
Rosso, Paolo
Potthast, Martin
Verhoeven, Ben
Daelemans, Walter
Description

We provide a training data set that consists of Twitter tweets in English, Spanish and Dutch.

The English and Spanish datasets are labeled with age and gender, whereas the Dutch one only with gender. With regard to age, we will consider the following classes: 18-24, 25-34, 35-49, 50-64, 65-xx.

Remark. Due to Twitter's privacy policy we cannot provide tweets directly, but only URLs referring to them. You will have to download them yourself. For your convenience, we provide a download software for this. We expect participants to extract gender and age information only from the textual part of a tweet and to discard any other meta information that may be provided by Twitter's API. When we evaluate your software at our site, we do not expect it downloads tweets. We will do this beforehand.

More information about the task: Link