Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification
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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
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6 more
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 2.
6 papers.
- 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 101
- 2022Detecting and Understanding Harmful Memes: A Surveycited by 58
- 2024Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicitiescited by 13
- 2023MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualizationcited by 11
- 2021Pretrained Transformers for Offensive Language Identification in Tanglishcited by 1
- 2021IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languagescited by 1
- 2024Survey of Cultural Awareness in Language Models: Text and Beyond30 Oct 2024
- 2024Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities11 Jun 2024
- 2023MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization25 May 2023
- 2022Detecting and Understanding Harmful Memes: A Survey9 May 2022
- 2021IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages15 Nov 2021
- 2021Pretrained Transformers for Offensive Language Identification in Tanglish6 Oct 2021