BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
- URL: http://arxiv.org/abs/2310.11748v1
- Date: Wed, 18 Oct 2023 07:10:47 GMT
- Title: BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
- Authors: Mithun Das and Animesh Mukherjee
- Abstract summary: A simple yet effective way of abusing individuals or communities is by creating memes.
Such harmful elements are in rampant use and are a threat to online safety.
It is necessary to develop efficient models to detect and flag abusive memes.
- Score: 11.04522597948877
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The dramatic increase in the use of social media platforms for information
sharing has also fueled a steep growth in online abuse. A simple yet effective
way of abusing individuals or communities is by creating memes, which often
integrate an image with a short piece of text layered on top of it. Such
harmful elements are in rampant use and are a threat to online safety. Hence it
is necessary to develop efficient models to detect and flag abusive memes. The
problem becomes more challenging in a low-resource setting (e.g., Bengali
memes, i.e., images with Bengali text embedded on it) because of the absence of
benchmark datasets on which AI models could be trained. In this paper we bridge
this gap by building a Bengali meme dataset. To setup an effective benchmark we
implement several baseline models for classifying abusive memes using this
dataset. We observe that multimodal models that use both textual and visual
information outperform unimodal models. Our best-performing model achieves a
macro F1 score of 70.51. Finally, we perform a qualitative error analysis of
the misclassified memes of the best-performing text-based, image-based and
multimodal models.
Related papers
- Decoding Memes: A Comparative Study of Machine Learning Models for Template Identification [0.0]
"meme template" is a layout or format that is used to create memes.
Despite extensive research on meme virality, the task of automatically identifying meme templates remains a challenge.
This paper presents a comprehensive comparison and evaluation of existing meme template identification methods.
arXiv Detail & Related papers (2024-08-15T12:52:06Z) - XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines.
We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning.
textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 3.71% and 4.82%, respectively.
arXiv Detail & Related papers (2024-07-24T10:51:46Z) - Deciphering Hate: Identifying Hateful Memes and Their Targets [4.574830585715128]
We introduce a novel dataset for detecting hateful memes in Bengali, BHM (Bengali Hateful Memes)
The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target.
To solve these tasks, we propose DORA, a multimodal deep neural network that systematically extracts the significant modality features from the memes.
arXiv Detail & Related papers (2024-03-16T06:39:41Z) - Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes
Through Multimodal Explanations [48.82168723932981]
We introduce em MultiBully-Ex, the first benchmark dataset for multimodal explanation from code-mixed cyberbullying memes.
A Contrastive Language-Image Pretraining (CLIP) approach has been proposed for visual and textual explanation of a meme.
arXiv Detail & Related papers (2024-01-18T11:24:30Z) - Explainable Multimodal Sentiment Analysis on Bengali Memes [0.0]
Understanding and interpreting the sentiment underlying memes has become crucial in the age of information.
This study employed a multimodal approach using ResNet50 and BanglishBERT and achieved a satisfactory result of 0.71 weighted F1-score.
arXiv Detail & Related papers (2023-12-20T17:15:10Z) - A Template Is All You Meme [83.05919383106715]
We release a knowledge base of memes and information found on www.knowyourmeme.com, composed of more than 54,000 images.
We hypothesize that meme templates can be used to inject models with the context missing from previous approaches.
arXiv Detail & Related papers (2023-11-11T19:38:14Z) - Unimodal Intermediate Training for Multimodal Meme Sentiment
Classification [0.0]
We present a novel variant of supervised intermediate training that uses relatively abundant sentiment-labelled unimodal data.
Our results show a statistically significant performance improvement from the incorporation of unimodal text data.
We show that the training set of labelled memes can be reduced by 40% without reducing the performance of the downstream model.
arXiv Detail & Related papers (2023-08-01T13:14:10Z) - Benchmarking Robustness of Multimodal Image-Text Models under
Distribution Shift [50.64474103506595]
We investigate the robustness of 12 popular open-sourced image-text models under common perturbations on five tasks.
Character-level perturbations constitute the most severe distribution shift for text, and zoom blur is the most severe shift for image data.
arXiv Detail & Related papers (2022-12-15T18:52:03Z) - DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally
Spreading Out Disinformation [72.18912216025029]
We present DisinfoMeme to help detect disinformation memes.
The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism.
arXiv Detail & Related papers (2022-05-25T09:54:59Z) - Multimodal Hate Speech Detection from Bengali Memes and Texts [0.6709991492637819]
This paper is about hate speech detection from multimodal Bengali memes and texts.
We train several neural networks to analyze textual and visual information for hate speech detection.
Our study suggests that memes are moderately useful for hate speech detection in Bengali, but none of the multimodal models outperform unimodal models.
arXiv Detail & Related papers (2022-04-19T11:15:25Z) - Caption Enriched Samples for Improving Hateful Memes Detection [78.5136090997431]
The hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not.
Both unimodal language models and multimodal vision-language models cannot reach the human level of performance.
arXiv Detail & Related papers (2021-09-22T10:57:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.