TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore

Nirmalendu, Prakash,; Hee, Ming Shan; Lee, Roy Ka-Wei

doi:10.1145/3587819.3592545

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Importantly, MATK integrates model analysis techniques that offer practitioners and researchers valuable insights into the strengths and weaknesses of their models. Our future plans for MATK include the following: (i) expanding dataset and model support to include the TotalDefMeme dataset [14], MET-Meme dataset [28], and the DisMultiHate model [5]; (ii) enabling multi-task training with different datasets, such as using FHM for hateful classification and HarMeme for harmful classification; (iii) enhancing the documentation to provide comprehensive and detailed information.…”

Section: Discussionmentioning

confidence: 99%

MATK: The Meme Analytical Tool Kit

Hee,

Kumaresan,

Hoang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

Self Cite

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The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results in this field. However, there is currently a lack of a single library that allows for the reproduction, evaluation, and comparison of these models using fair benchmarks and settings. To fill this gap, we introduce the Meme Analytical Tool Kit (MATK), an open-source toolkit specifically designed to support existing memes datasets and cutting-edge multimodal models. MATK aims to assist researchers and engineers in training and reproducing these multimodal models for meme classification tasks, while also providing analysis techniques to gain insights into their strengths and weaknesses. To access MATK, please visit https://github.com/Social-AI-Studio/MATK. CCS CONCEPTS• Computing methodologies → Natural language processing; Computer vision representations.

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Section: Discussionmentioning

confidence: 99%

MATK: The Meme Analytical Tool Kit

Hee,

Kumaresan,

Hoang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the context of Singaporean meme analysis, the TotalDefMeme dataset [8], another resource cited on the challenge website, illustrates the complexity of this task. The dataset was annotated by three annotators per meme, revealing significant discrepancies in their interpretations, despite all annotators being from the same cultural background.…”

Section: Harmfulness Classificationmentioning

confidence: 99%

OSPC: Artificial VLM Features for Hateful Meme Detection

Grönquist

2024

Companion Proceedings of the ACM Web Conference 2024

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The digital revolution and the advent of the world wide web have transformed human communication, notably through the emergence of memes. While memes are a popular and straightforward form of expression, they can also be used to spread misinformation and hate due to their anonymity and ease of use. In response to these challenges, this paper introduces a solution developed by team 'Baseline' for the AI Singapore Online Safety Prize Challenge. Focusing on computational efficiency and feature engineering, the solution achieved an AUROC of 0.76 and an accuracy of 0.69 on the test dataset. As key features, the solution leverages the inherent probabilistic capabilities of large Vision-Language Models (VLMs) to generate task-adapted feature encodings from text, and applies a distilled quantization tailored to the specific cultural nuances present in Singapore. This type of processing and fine-tuning can be adapted to various visual and textual understanding and classification tasks, and even applied on private VLMs such as OpenAI's GPT. Finally it can eliminate the need for extensive model training on large GPUs for resource constrained applications, also offering a solution when little or no data is available.

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“…The identification of these sensitive topics aids in effective intervention and prevention. • Total Defence Memes (TotalDefMeme) [25]: TotalDefMeme features locally relevant topics related to Singapore's Total Defence concept, providing valuable insights into the associated events and activities.…”

Section: Experiments Settingsmentioning

confidence: 99%

PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models

Prakash,

Wang,

Hoang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey meaning, humor, and cultural significance. While meme analysis has been an active area of research, little work has been done on unsupervised multimodal topic modeling of memes, which is important for content moderation, social media analysis, and cultural studies. We propose PromptMTopic, a novel multimodal prompt-based model designed to learn topics from both text and visual modalities by leveraging the language modeling capabilities of large language models. Our model effectively extracts and clusters topics learned from memes, considering the semantic interaction between the text and visual modalities. We evaluate our proposed model through extensive experiments on three real-world meme datasets, which demonstrate its superiority over state-of-the-art topic modeling baselines in learning descriptive topics in memes. Additionally, our qualitative analysis shows that PromptMTopic can identify meaningful and culturally relevant topics from memes. Our work contributes to the understanding of the topics and themes of memes, a crucial form of communication in today's society. Disclaimer: This paper contains sensitive content that may be disturbing to some readers. CCS CONCEPTS• Computing methodologies → Natural language processing; Computer vision representations.

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TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore

Cited by 6 publications

References 12 publications

MATK: The Meme Analytical Tool Kit

MATK: The Meme Analytical Tool Kit

OSPC: Artificial VLM Features for Hateful Meme Detection

PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models

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