# SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!

arXiv: https://arxiv.org/abs/2008.03781 · HTML: https://mlsanity.com/paper/2008.03781/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 30 of 78)

- [MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2109.05184/) (2021)
- [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021)
- [MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms](https://mlsanity.com/paper/2402.14154/) (2024)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning](https://mlsanity.com/paper/2311.08110/) (2023)
- [What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes](https://mlsanity.com/paper/2212.00715/) (2022)
- [MIEB: Massive Image Embedding Benchmark](https://mlsanity.com/paper/2504.10471/) (2025)
- [Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?](https://mlsanity.com/paper/2301.11219/) (2023)
- [AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media](https://mlsanity.com/paper/2106.11229/) (2021)
- [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024)
- [Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark](https://mlsanity.com/paper/2109.01839/) (2021)
- [TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore](https://mlsanity.com/paper/2305.17911/) (2023)
- [MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing](https://mlsanity.com/paper/2405.11215/) (2024)
- [Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study](https://mlsanity.com/paper/2505.15389/) (2025)
- [MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization](https://mlsanity.com/paper/2305.15913/) (2023)
- [Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis](https://mlsanity.com/paper/2103.12377/) (2021)
- [MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation](https://mlsanity.com/paper/2403.14652/) (2024)
- [HateProof: Are Hateful Meme Detection Systems really Robust?](https://mlsanity.com/paper/2302.05703/) (2023)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [Memotion Analysis through the Lens of Joint Embedding](https://mlsanity.com/paper/2111.07074/) (2021)
- [MemeFier: Dual-stage Modality Fusion for Image Meme Classification](https://mlsanity.com/paper/2304.02906/) (2023)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [MemeLens: Multilingual Multitask VLMs for Memes](https://mlsanity.com/paper/2601.12539/) (2026)
- [Multimodal Analysis of memes for sentiment extraction](https://mlsanity.com/paper/2112.11850/) (2021)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models](https://mlsanity.com/paper/2312.06093/) (2023)
- [TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes](https://mlsanity.com/paper/2204.06299/) (2022)
- [Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories](https://mlsanity.com/paper/2308.10562/) (2023)
- [Chumor 2.0: Towards Benchmarking Chinese Humor Understanding](https://mlsanity.com/paper/2412.17729/) (2024)
- [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024)

## Abstract (arXiv)

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, the computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twiter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes, with human-annotated labels namely sentiment (positive, negative, neutral), type of emotion (sarcastic, funny, offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humour, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.