Synthesizing Up-to-date Publications & bEnchmarks for Researchers
in Multimedia Automatic Misogyny Identification
A central hub for our research, papers, datasets, and resources on misogyny detection in online multimedia content.
- 💡 About
- 📚 Publications
- 📊 Datasets
- 👩🔬 Team
- ⭐ Cite & Support
SUPER MAMI is a dissemination hub for our work on detecting and understanding misogyny in multimedia content.
We focus on:
- 🕵️♀️ Detection of misogynistic language and content
- 📚 Understanding patterns across platforms, languages, and modalities
- 🤝 Providing datasets, benchmarks, and papers for the research community
Our goal is to advance knowledge, support reproducibility, and foster collaboration in the study of online misogyny.
This collection showcases our ongoing research into the pervasive issues of sexism and misogyny, particularly within digital and social contexts. Our work spans various facets, including:
🎯 Detection and Classification: Developing and refining computational models to identify and categorize both unimodal and multimodal misogynistic content.
🎯 Bias Estimation: Measuring and quantifying biases in language models and (unimodal and multimodal) datasets related to gender, sexism, and misogyny.
🎯 Bias Mitigation: Designing approaches to reduce bias and improve fairness in automated systems for detecting misogynistic content.
📚 "Benchmark dataset of memes with text transcriptions for automatic detection of multimodal misogynistic content"
```
@article{gasparini2022benchmark,
title={Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content},
author={Gasparini, Francesca and Rizzi, Giulia and Saibene, Aurora and Fersini, Elisabetta},
journal={Data in brief},
volume={44},
pages={108526},
year={2022},
publisher={Elsevier}
}
```
📚 "Misogynous meme recognition: A preliminary study"
```
@inproceedings{fersini2021misogynous,
title={Misogynous meme recognition: A preliminary study},
author={Fersini, Elisabetta and Rizzi, Giulia and Saibene, Aurora and Gasparini, Francesca},
booktitle={International conference of the Italian association for artificial intelligence},
pages={279--293},
year={2021},
organization={Springer}
}
```
📚 "SemEval-2022 Task 5: Multimedia automatic misogyny identification"
```
@inproceedings{fersini2022semeval,
title={SemEval-2022 Task 5: Multimedia automatic misogyny identification},
author={Fersini, Elisabetta and Gasparini, Francesca and Rizzi, Giulia and Saibene, Aurora and Chulvi, Berta and Rosso, Paolo and Lees, Alyssa and Sorensen, Jeffrey},
booktitle={Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022">},
pages={533--549},
year={2022}
}
```
📚 "Bias mitigation in misogynous meme recognition: A preliminary study"
```
@inproceedings{balducci2023bias,
title={Bias Mitigation in Misogynous Meme Recognition: A Preliminary Study},
author={Balducci, Gianmaria and Rizzi, Giulia and Fersini, Elisabetta},
booktitle={Proceedings of the 9th Italian Conference on Computational Linguistics (CLiC-it 2023">},
pages={63--69},
year={2023}
}
```
📚 "Recognizing misogynous memes: Biased models and tricky archetypes"
```
@article{rizzi2023recognizing,
title={Recognizing misogynous memes: Biased models and tricky archetypes},
author={Rizzi, Giulia and Gasparini, Francesca and Saibene, Aurora and Rosso, Paolo and Fersini, Elisabetta},
journal={Information Processing \& Management},
volume={60},
number={5},
pages={103474},
year={2023},
publisher={Elsevier}
}
```
📚 "Multimodal Hate Speech Detection in Memes from Mexico using BLIP"
```
@article{maqbool2024multimodal,
title={Multimodal Hate Speech Detection in Memes from Mexico using BLIP},
author={Maqbool, Fariha and Fersini, Elisabetta},
year={2024}
}
```
📚 "A contrastive learning based approach to detect sexism in memes"
```
@article{maqbool2024contrastive,
title={A contrastive learning based approach to detect sexism in memes},
author={Maqbool, Fariha and Fersini, Elisabetta},
journal={Working Notes of CLEF},
year={2024}
}
```
📚 "From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes"
```
@inproceedings{rizzi2024explanation,
title={From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes},
author={Rizzi, Giulia and Rosso, Paolo and Fersini, Elisabetta},
booktitle={Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024">}},
pages={821--828},
year={2024}
}
```
📚 "Misogynous Memes Recognition: Training vs Inference Bias Mitigation Strategies"
```
@article{balducci2025misogynous,
title={Misogynous Memes Recognition: Training vs Inference Bias Mitigation Strategies},
author={Balducci, Gianmaria and Rizzi, Giulia and Fersini, Elisabetta},
journal={IJCoL. Italian Journal of Computational Linguistics},
volume={11},
number={11-1},
year={2025},
publisher={Accademia University Press}
}
```
📚 "Misogynous Memes Recognition: Training vs Inference Bias Mitigation Strategies"
```
@article{rizzibeyond,
title={Beyond Misogyny Detection: Investigating Bias and Embracing Perspectivism},
author={Rizzi, Giulia and Fersini, Elisabetta},
journal={Book of Abstracts _ Data Science & Social Research (DSSR 2025)},
pages={13}
}
```
Our Participation in EXIST Shared Tasks :
📚 "AI-UPV at EXIST 2023--Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime"
```
@inproceedings{de2023ai,
title={AI-UPV at EXIST 2023--Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime},
author={de Paula, A and Rizzi, G and Fersini, E and Spina, D and others},
booktitle={CEUR WORKSHOP PROCEEDINGS},
volume={3497},
pages={985--999},
year={2023},
organization={CEUR-WS}
}
```
📚 "PINK at EXIST2024: a cross-lingual and multi-modal transformer approach for sexism detection in memes"
```
@@article{rizzi2024pink,
title={PINK at EXIST2024: a cross-lingual and multi-modal transformer approach for sexism detection in memes},
author={Rizzi, Giulia and Gimeno-G{\'o}mez, David and Fersini, Elisabetta and Mart{\'\i}nez-Hinarejos, Carlos-D},
journal={Working Notes of CLEF},
year={2024}
}
```
Our datasets may be distributed upon request and for academic purposes only. To request the datasets, please fill out the respective forms:
-
🇬🇧 Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content
-
🇬🇧 MAMI - Multimedia Automatic Misogyny Identification
📥 Request Form · 📘 Paper -
🇮🇹 MAMITA: Benchmarking Misogyny in Italian Memes
📥 Request Form · 📘 Paper
| Name | Language | Type of Data | Number of Data | Task | Annotators | Perspectivist Evaluation | Additional Info |
|---|---|---|---|---|---|---|---|
| Benchmark | 🇬🇧 | MEME | 800 | A: Misogyny detection and B: Misogyny type | 👥 crowd and 🎓 Domain Experts | ❌ | ❌ |
| MAMI | 🇬🇧 | MEME | 10k + 1k | A: Misogyny detection and B: Misogyny type | 👥 crowd | ❌ | ❌ |
| MAMITA | 🇮🇹 | MEME | 1880 | A: Misogyny detection and B: Misogyny type | 👥 crowd and 🎓 Domain Experts | ✅ | Demographic and socio-cultural background |
Created with ❤️ by the MAMI Research Team
If you use our datasets or papers, please cite our work.
Don’t forget to star this repository to support our research!
