| Challenge: | Recent studies on sentiment analysis of memes have focused on English, but there is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. |
| Approach: | They propose to use a Bengali dataset to perform multimodal sentiment analysis in low resource languages. |
| Outcome: | The proposed dataset for Bengali contains 4417 memes with three annotated labels positive, negative, and neutral. |
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| Challenge: | social media has enabled information propagation at unprecedented rate, but also generated malign content, such as hateful memes . a multimodal hate speech dataset is used to study the impact of hateful content on society . current studies focus on monolingual memes, but existing models cannot provide accurate inferences based on code-mixed captions a study on Bengali memes shows that joint evaluation of visual and textual features significantly improves the hateful data classification . |
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| Outcome: | The proposed dataset shows that evaluation of visual and textual features significantly improves the hateful memes classification compared to unimodal evaluation. |
A Multimodal Framework to Detect Target Aware Aggression in Memes (2024.eacl-long)
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| Challenge: | Recent research on memes’ detrimental facets is skewed towards high-resource languages, such as Bengali. |
| Approach: | They propose a dataset MIMOSA that annotates annotated memes across five aggression target categories in Bengali and propose 'Multimodal Attentive Fusion' to detect aggression targets. |
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Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
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| 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. |
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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
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Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
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BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification (2023.emnlp-main)
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| 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 . |
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M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets (2024.lrec-main)
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| Challenge: | Existing studies on sentiment analysis of tweets focus on the English language . however, there is still a challenge of processing lower-resourced languages . |
| Approach: | They transform tweet sentiment dataset into a multimodal format through a straightforward curation process. |
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Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)
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| Challenge: | Sentiment analysis in social media is challenging because of the lack of context. |
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| Outcome: | The proposed model performs best compared with other models. |
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification (2024.emnlp-main)
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| Challenge: | a novel dataset of text-embedded images associated with the LGBTQ+ Pride movement is presented in this paper . a new framework for analyzing text-based images is proposed to address this challenge . |
| Approach: | They propose a new dataset for machine learning that includes hate, targets of hate, stance, humor and a framework for efficient downstream learning while preserving the knowledge of the pre-trained CLIP model. |
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FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
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| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
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Social Meme-ing: Measuring Linguistic Variation in Memes (2024.naacl-long)
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| Challenge: | In this paper, we analyze memes as a form of language subject to the same kinds of sociolinguistic variation as other modalities, such as written language and speech. |
| Approach: | They propose a computational pipeline to cluster memes into templates and semantic variables, taking advantage of their multimodal structure to learn meme semantics from an unstructured dataset. |
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