@inproceedings{kannan-rajalakshmi-2022-multimodal,
title = "Multimodal Code-Mixed {T}amil Troll Meme Classification using Feature Fusion",
author = "Kannan, Ramesh and
Rajalakshmi, Ratnavel",
editor = "Chakravarthi, Bharathi Raja and
Murugappan, Abirami and
Chinnappa, Dhivya and
Hane, Adeep and
Kumeresan, Prasanna Kumar and
Ponnusamy, Rahul",
booktitle = "Proceedings of the First Workshop on Multimodal Machine Learning in Low-resource Languages",
month = dec,
year = "2022",
address = "IIIT Delhi, New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mmlow-1.1",
pages = "1--8",
abstract = "Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.",
}
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<abstract>Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.</abstract>
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%0 Conference Proceedings
%T Multimodal Code-Mixed Tamil Troll Meme Classification using Feature Fusion
%A Kannan, Ramesh
%A Rajalakshmi, Ratnavel
%Y Chakravarthi, Bharathi Raja
%Y Murugappan, Abirami
%Y Chinnappa, Dhivya
%Y Hane, Adeep
%Y Kumeresan, Prasanna Kumar
%Y Ponnusamy, Rahul
%S Proceedings of the First Workshop on Multimodal Machine Learning in Low-resource Languages
%D 2022
%8 December
%I Association for Computational Linguistics
%C IIIT Delhi, New Delhi, India
%F kannan-rajalakshmi-2022-multimodal
%X Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.
%U https://aclanthology.org/2022.mmlow-1.1
%P 1-8
Markdown (Informal)
[Multimodal Code-Mixed Tamil Troll Meme Classification using Feature Fusion](https://aclanthology.org/2022.mmlow-1.1) (Kannan & Rajalakshmi, MMLow 2022)
ACL