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Published February 28, 2020 | Version 0.0.1
Dataset Restricted

PAN20 Authorship Analysis: Celebrity Profiling

  • 1. Bauhaus-Universität Weimar
  • 2. Universität Leipzig



  • Task: Given the Twitter feeds of the followers, determine the occupation, age, and gender of a celebrity.
  • Evaluation: [code]
  • Baselines: [code]
  • See the full Shared Task [here]

The datasets contain three files: a follower-feeds.ndjson as input, a labels.ndjson as output, and a celebrity-feeds.ndjson for additional study. Each file lists all celebrities as JSON objects, one per line and identified by the id key. The training dataset contains 1,920 celebrities and is balanced towards gender and occupation. The supplement dataset contains the remaining 8,265 celebrities but is not balanced in any way.


The follower-feeds.ndjson contains the English tweets of at least 10 followers for each celebrity, with at least 50 tweets each excluding retweets.

{"id": 1234, "text": [["a tweet of follower 1", "another tweet of follower 1", ...], ["a tweet of follower 2", ...], ...]}
{"id": 5678, "text": [["a tweet of follower 1", "another tweet of follower 1", ...], ["a tweet of follower 2", ...], ...]}


The celebrity-feeds.ndjson contains the Twitter timelines of the original celebrities, formatted as:

{"id": 1234, "text": ["a tweet of celebrity 1", "another tweet of celebrity 1", ...]}
{"id": 5678, "text": ["a tweet of celebrity 2", "another tweet", ...]}


The labels.ndjson contains the classes that should be predicted. A valid submission has to produce a labels.ndjson given the follower-feeds.ndjson and contain an entry for each id given in the input.

{"id": 1234, "occupation": "sports", "gender": "female", "birthyear": 2002}
{"id": 5678, "occupation": "professional", "gender": "male", "birthyear": 1990}

The following values are possible for each of the traits:

occupation  := {sports, performer, creator, politics}
birthyear   := {1940, ..., 1999}
gender      := {male, female}




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