Task 7: Hahackathon: Linking Humor and Offense Across Different Age Groups

Background and Motivation

Humor, like most figurative language, poses interesting linguistic challenges to NLP, due to its emphasis on multiple word senses, cultural knowledge, and pragmatic competence. Humor appreciation is also a highly subjective phenomenon, with age, gender and socio-economic status known to have an impact on the perception of a joke. In this task, we collected labels and ratings from a balanced set of age groups from 18-70. Our annotators also represented a variety of genders, political stances and income levels.
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Tasks

  • Task 1 emulates previous humor detection tasks in which all ratings were averaged to provide mean classification and rating scores.

    • Task 1a: predict if the text would be considered humorous (for an average user). This is a binary task.
    • Task 1b: if the text is classed as humorous, predict how humorous it is (for an average user). The values vary between 0 and 5.
    • Task 1c: if the text is classed as humorous, predict if the humor rating would be considered controversial, i.e. the variance of the rating between annotators is higher than the median. This is a binary task.
  • Task 2 aims to predict how offensive a text would be (for an average user) with values between 0 and 5.

    • Task 2a: predict how generally offensive a text is for users. This score was calculated regardless of whether the text is classed as humorous or offensive overall.

Organizers

J. A. Meaney, University of Edinburgh, jameaney@ed.ac.uk
Steven Wilson, University of Edinburgh, steven.wilson@ed.ac.uk
Luis Chiruzzo, Universidad de la Republica, luischir@fing.edu.uy
Walid Magdy , University of Edinburgh, wmagdy@inf.ed.ac.uk

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Hahackathon Results

Data coming shortly