Synopsis

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}

This dataset includes all the processed data used for experimentation in the article "Perfilado Demográficos de Celebridades en Redes Sociales" - "Demographic Profiling of Celebrities in Social Networks", published in the journal Research in Computer Science. The dataset is a processed version of the training part from the CLEF 2020 celebrity profiling task (https://pan.webis.de/clef20/pan20-web/celebrity-profiling.html). The dataset consists of 5,066,608 tweets corresponding to 1,920 Twitter celebrities. All the tweets are in English. The dataset includes several files:

1. The 5,066,608 tweets in English

2. Four files indicating the gender, age, ocuppation and user associated with each tweet.

3. A list of 1374 common english abreviations used in social networks

4. The five features extracted from the tweets and used for the experiments: words, emoticons/emojis, hashtags, ats, abreviations