🗯 About the Project
Sexism is an omnipresent societal issue that has escalated in social media in particular. Recent studies show that especially women in the public domain face significant challenges. Social media act as an accelerator that lowers the inhibition threshold for verbal harassment.
Social networks are the main platforms for social complaint, activism, etc. where movements like #MeToo, #8M or #Time’sUp have spread rapidly. Under the umbrella of social networks, many women all around the world have reported abuses, discriminations and other sexist experiences suffered in real life. Social networks are also contributing to the transmission of sexism and other disrespectful and hateful behaviours. Even though social platforms such as Twitter are continuously creating new ways to identify and eradicate hateful content, they are facing many difficulties when dealing with the huge amount of data generated by users. In this context, automatic tools not only may help to detect and alert against sexism behaviours and discourses, but also to estimate how often sexist and abusive situations are found in social media platforms, what forms of sexism are more frequent and how sexism is expressed in these media.
EXIST is a series of scientific events and shared tasks on sexism identification in social networks. It aims to capture sexism in a broad sense, from explicit misogyny to other subtle expressions that involve implicit sexist behaviors (EXIST 2021, EXIST 2022). The third edition of the EXIST shared task will be held as a Lab in CLEF 2023, on September 18-21, 2023, in the Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece.
🙌 Tasks & Results
Binary detection of sexism:
Systems must determine whether a specific tweet contains sexist expressions or behaviours.
Ternary classification
Classifying sexist content based on the authors’ intent. St. Pölten UAS/AIT ranking: 1st place for Spanish content & 2nd place for Spanish and English content.
Multi-label classification:
We need to classify sexist content into one or more of the following categories: ideology and inequality, stereotyping and dominance, objectification, sexual violence, and misogyny and non-sexual violence. St. Pölten UAS/AIT ranking: 3rd place for Spanish and English content.
Read more about the project here.
The paper is available online via https://www.dei.unipd.it/~faggioli/temp/CLEF2023-proceedings/paper-074.pdf.
✨ Team
Andreas Babic (FHSTP)
Jaqu Böck (FHSTP)
Daria Liakhovets (AIT)
Nathanya Queby Satriani (FHSTP)
Alexander Schindler (AIT)
Mina Schütz (AIT)
Djordje Slijepčević (FHSTP)
Matthias Zeppelzauer (FHSTP)
Affiliation(s)
