PAN 22 Author Profiling: Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)

TASK

With irony, language is employed in a figurative and subtle way to mean the opposite to what is literally stated. In case of sarcasm, a more aggressive type of irony, the intent is to mock or scorn a victim without excluding the possibility to hurt. Stereotypes are often used, especially in discussions about controversial issues such as immigration or sexism and misogyny. At PAN’22, we will focus on profiling ironic authors in Twitter. Special emphasis will be given to those authors that employ irony to spread stereotypes, for instance, towards women or the LGTB community. The goal will be to classify authors as ironic or not depending on their number of tweets with ironic content. Among those authors we will consider a subset that employs irony to convey stereotypes in order to investigate if state-of-the-art models are able to distinguish also these cases. Therefore, given authors of Twitter together with their tweets, the goal will be to profile those authors that can be considered as ironic.

DATA

Input

The uncompressed dataset consists in a folder which contains:

• A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id.
• A truth.txt file with the list of authors and the ground truth.

The format of the XML files is:

    <author lang="en">
    <documents>
    <document>Tweet 1 textual contents</document>
    <document>Tweet 2 textual contents</document>
    ...
    </documents>
    </author>

The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.

    2d0d4d7064787300c111033e1d2270cc:::I
    b9eccce7b46cc0b951f6983cc06ebb8:::NI
    f41251b3d64d13ae244dc49d8886cf07:::I
    47c980972060055d7f5495a5ba3428dc:::NI
    d8ed8de45b73bbcf426cdc9209e4bfbc:::I
    2746a9bf36400367b63c925886bc0683:::NI
    ...

Evaluation

The performance of your system will be ranked by accuracy.

More info on the task: https://pan.webis.de/clef22/pan22-web/author-profiling.html