From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes (2024.lrec-main)
Copied to clipboard
Rahul Ponnusamy, Kathiravan Pannerselvam, Saranya R, Prasanna Kumar Kumaresan, Sajeetha Thavareesan, Bhuvaneswari S, Anshid K.a, Susminu S Kumar, Paul Buitelaar, Bharathi Raja Chakravarthi
| Challenge: | a new form of memes has emerged to combat misogyny and harmful stereotypes . authors present a dataset to analyze online misogamy in Tamil and Malayalam communities . |
| Approach: | They propose to create an annotated dataset with detailed annotation guidelines to analyze online misogyny within Tamil and Malayalam-speaking communities. |
| Outcome: | The proposed dataset reveals the world of gender bias and stereotypes in Tamil and Malayalam-speaking communities. |
Similar Papers
Unintended Bias Detection and Mitigation in Misogynous Memes (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing models that detect misogyny are not able to detect unintended biases in memes, perpetuating harmful stereotypes and reinforcing negative attitudes. |
| Approach: | They propose to measure and mitigate unintentional bias in misogynous memes detection models by using a contextualized scene graph-based multimodal network (CTXSGMNet) they also evaluate their generalizability by evaluating their performance on a few benchmark meme datasets. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SemEval-2022 Task 5 (MAMI task) dataset, showcasing its promising performance in terms of Equity of Odds and F1 score. |
An Expert Annotated Dataset for the Detection of Online Misogyny (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies have found that misogynistic content is pervasive on some Reddit communities, but a training dataset for misogorical classification has not been created with the data. |
| Approach: | They propose a hierarchical taxonomy and an expert labelled dataset to enable automatic classification of online misogynistic content. |
| Outcome: | The proposed taxonomy and an expert labelled dataset are made freely available for future research. |
Annotating Online Misogyny (2021.acl-long)
Copied to clipboard
| Challenge: | Online misogyny is a category of online abusive language with serious and harmful social consequences. |
| Approach: | They propose an iterative annotation process and a taxonomy of labels for annotating misogyny in natural written language and cite a high-quality dataset of annotated posts from social media posts. |
| Outcome: | The proposed method aims to identify misogynistic language in natural written language and annotate it in social media posts using a high-quality dataset. |
BANMIME : Misogyny Detection with Metaphor Explanation on Bangla Memes (2025.emnlp-main)
Copied to clipboard
Md Ayon Mia, Akm Moshiur Rahman Mazumder, Khadiza Sultana Sayma, Md Fahim, Md Tahmid Hasan Fuad, Muhammad Ibrahim Khan, Akmmahbubur Rahman
| Challenge: | Existing studies have explored hate speech and general meme classification, but the nuanced identification of misogyny in Bangla memes remains underexplored. |
| Approach: | They propose a Bangla misogynistic meme dataset that includes misos, humor, metaphors and detailed human-written explanations. |
| Outcome: | The proposed dataset is the first comprehensive dataset of misogynistic Bangla memes . it includes misos, humor categories, metaphor localization, and detailed human-written explanations based on 2,000 culturally grounded samples . |
CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on detecting offensive memes have focused on identifying them as implicit and explicit . detecting memes requires contextual knowledge, but there is no such dataset for the code-mixed Hindi-English domain. |
| Approach: | They propose an end-to-end multitask model that integrates contextual knowledge and psycho-linguistic knowledge to detect offensive memes. |
| Outcome: | The proposed model is able to detect offensive memes and explicit memes in a large-scale dataset. |
Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
Copied to clipboard
| Challenge: | a growing body of research has focused on the negative aspects of memes in high-resource languages like Bengali . a new dataset for Bengali hateful memes is designed to detect their targeted entities . |
| Approach: | They propose a multimodal dataset that analyzes the modality of memes and compares them with other datasets. |
| Outcome: | The proposed dataset outperforms state-of-the-art datasets on Bengali hateful memes . the proposed dataset is generalizable on other low-resource hateful memes datasets compared with baselines based on the proposed model . |
BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification (2023.emnlp-main)
Copied to clipboard
| Challenge: | a number of studies have tried to detect and control the spread of such abusive memes on social media platforms. |
| Approach: | They build a Bengali meme dataset to test models for abusive memes . they find that multimodal models that use both textual and visual information outperform unimodal models . |
| Outcome: | The proposed model outperforms unimodal models in a Bengali meme dataset. |
Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation (2024.findings-acl)
Copied to clipboard
Brooklyn Sheppard, Anna Richter, Allison Cohen, Elizabeth Smith, Tamara Kneese, Carolyne Pelletier, Ioana Baldini, Yue Dong
| Challenge: | the Biasly dataset captures misogyny in movies in ways unique within the literature. |
| Approach: | The Biasly dataset captures misogyny in North American film by combining annotations of movie subtitles with common NLP algorithms. |
| Outcome: | The Biasly dataset captures misogyny expressions in North American film . it contains annotations of movie subtitles and text generation for rewrites . |
MemeWeaver: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to detect hate speech on social media are limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. |
| Approach: | They propose a multimodal framework for detecting sexism and misogyny using a graph reasoning mechanism that can be used to train multiple visual-textual fusion strategies. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on MAMI and EXIST benchmarks while achieving faster training convergence. |
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
Copied to clipboard
Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
| Challenge: | 59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms. |
| Approach: | They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. |
| Outcome: | The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English. |