mlsanityData to 7 Oct 2026

Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification

arXiv 2108.03886 ↗First posted 9 Aug 2021cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

A meme is an part of media created to share an opinion or emotion across the internet. Due to its popularity, memes have become the new forms of communication on social media. However, due to its nature, they are being used in harmful ways such as trolling and cyberbullying progressively. Various data modelling methods create different possibilities in feature extraction and turning them into beneficial information. The variety of modalities included in data plays a significant part in predicting the results. We try to explore the significance of visual features of images in classifying memes. Memes are a blend of both image and text, where the text is embedded into the image. We try to incorporate the memes as troll and non-trolling memes based on the images and the text on them. However, the images are to be analysed and combined with the text to increase performance. Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features. The fine-tuned cross-lingual language model, XLM, performed the best in textual analysis, and the multimodal transformer performs the best in multimodal analysis.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 9 Aug 2021.

Builds on

18 of 46 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  7. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  8. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  9. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
  10. 2019How multilingual is Multilingual BERT?cited by 642
  11. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  12. 2015An Introduction to Convolutional Neural Networkscited by 510
6 more
  1. 2021MuRIL: Multilingual Representations for Indian Languagescited by 177
  2. 2020Processing South Asian Languages Written in the Latin Script: the Dakshina Datasetcited by 35
  3. 2019EmbraceNet: A robust deep learning architecture for multimodal classificationcited by 30
  4. 2018Multimodal Sentiment Analysis To Explore the Structure of Emotionscited by 19
  5. 2021UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attentioncited by 5
  6. 2021NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 2.

6 papers.