Unveiling misogyny memes: a multimodal analysis of modality effects on identification

Shijing Chen, Usman Naseem, Imran Razzak, Flora Salim

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In today’s digital era, memes have become a popular means of communication that often reflect societal attitudes as well as prejudices. Misogyny memes are a form of memes that explicitly discriminate against women in various aspects, such as shaming or stereotyping. This research aims to identify misogynous memes through deep learning multimodal analysis and determine which modality, text or image, plays a more significant role in fairness considerations. To achieve this, we utilized the dataset GOAT-benchmarks, which comprises over 6,000 diverse memes covering topics like implicit hate speech, sexism, and cyberbullying. Furthermore, we evaluated the fairness of these models by assessing their performance across different demographic groups. Our findings revealed that while both text and image modalities contribute to identifying misogynous memes, text plays a significant role in misogyny identification, while image contributes further in terms of fairness. This study emphasizes the importance of multimodal analysis in recognizing and mitigating biases in online content.

Disclaimer: This paper contains content that may be disturbing to some readers.

Original languageEnglish
Title of host publicationWWW '24 Companion
Subtitle of host publicationCompanion proceedings of the ACM on Web Conference 2024
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1864-1871
Number of pages8
ISBN (Electronic)9798400701726
DOIs
Publication statusPublished - 2024
Event33rd ACM Web Conference, WWW 2024 - Singapore, Singapore
Duration: 13 May 202417 May 2024

Conference

Conference33rd ACM Web Conference, WWW 2024
Country/TerritorySingapore
CitySingapore
Period13/05/2417/05/24

Keywords

  • Harassment
  • Misogyny Identification
  • Multimodal Analysis
  • Bias

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