TIB-VA at SemEval-2022 Task 5

A Multimodal Architecture for the Detection and Classification of Misogynous Memes

authored by

Sherzod Hakimov, Gullal S. Cheema, Ralph Ewerth

Abstract

The detection of offensive, hateful content on social media is a challenging problem that affects many online users on a daily basis. Hateful content is often used to target a group of people based on ethnicity, gender, religion and other factors. The hate or contempt toward women has been increasing on social platforms. Misogynous content detection is especially challenging when textual and visual modalities are combined to form a single context, e.g., an overlay text embedded on top of an image, also known as meme. In this paper, we present a multimodal architecture that combines textual and visual features to detect misogynous memes. The proposed architecture is evaluated in the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification challenge under the team name TIB-VA. We obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous and further identify the following sub-classes: shaming, stereotype, objectification, and violence.

Organisation(s)

L3S Research Centre

External Organisation(s)

German National Library of Science and Technology (TIB)

Type

Conference contribution

Pages

756-760

No. of pages

5

Publication date

07.2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Computer Science Applications, Theoretical Computer Science

Sustainable Development Goals

SDG 5 - Gender Equality, SDG 16 - Peace, Justice and Strong Institutions

Electronic version(s)

https://doi.org/10.48550/arXiv.2204.06299 (Access: Open)
https://doi.org/10.18653/v1/2022.semeval-1.105 (Access: Open)