A Multimodal Framework to Detect Target Aware Aggression in Memes
Shawly Ahsan, Eftekhar Hossain, Omar Sharif, Avishek Das, Mohammed Moshiul Hoque, M. Dewan
Abstract
Internet memes have gained immense traction as a medium for individuals to convey emotions, thoughts, and perspectives on social media. While memes often serve as sources of humor and entertainment, they can also propagate offensive, incendiary, or harmful content, deliberately targeting specific individuals or communities. Identifying such memes is challenging because of their satirical and cryptic characteristics. Most contemporary research on memes’ detrimental facets is skewed towards high-resource languages, often sidelining the unique challenges tied to low-resource languages, such as Bengali. To facilitate this research in low-resource languages, this paper presents a novel dataset MIMOSA (MultIMOdal aggreSsion dAtaset) in Bengali. MIMOSA encompasses 4,848 annotated memes across five aggression target categories: Political, Gender, Religious, Others, and non-aggressive. We also propose MAF (Multimodal Attentive Fusion), a simple yet effective approach that uses multimodal context to detect the aggression targets. MAF captures the selective modality-specific features of the input meme and jointly evaluates them with individual modality features. Experiments on MIMOSA exhibit that the proposed method outperforms several state-of-the-art rivaling approaches. Our code and data are available at https://github.com/shawlyahsan/Bengali-Aggression-Memes.- Anthology ID:
- 2024.eacl-long.153
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2487–2500
- Language:
- URL:
- https://aclanthology.org/2024.eacl-long.153
- DOI:
- Cite (ACL):
- Shawly Ahsan, Eftekhar Hossain, Omar Sharif, Avishek Das, Mohammed Moshiul Hoque, and M. Dewan. 2024. A Multimodal Framework to Detect Target Aware Aggression in Memes. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2487–2500, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- A Multimodal Framework to Detect Target Aware Aggression in Memes (Ahsan et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2024.eacl-long.153.pdf