TIB-VA at SemEval-2022 Task 5

A Multimodal Architecture for the Detection and Classification of Misogynous Memes

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Aufsatz in Konferenzband

Seiten

756-760

Anzahl der Seiten

5

Publikationsdatum

07.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Angewandte Informatik, Theoretische Informatik

Ziele für nachhaltige Entwicklung

SDG 5 – Gleichberechtigung der Geschlechter, SDG 16 – Frieden, Gerechtigkeit und starke Institutionen

Elektronische Version(en)

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