@inproceedings{nandi-etal-2022-teamx,
title = "{T}eam{X}@{D}ravidian{L}ang{T}ech-{ACL}2022: A Comparative Analysis for Troll-Based Meme Classification",
author = "Nandi, Rabindra Nath and
Alam, Firoj and
Nakov, Preslav",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.13",
doi = "10.18653/v1/2022.dravidianlangtech-1.13",
pages = "79--85",
abstract = "The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social mediaplatforms, government agencies, policymakers, and society as a whole. This is because such harmful or abusive content leads to several consequences to people such as physical, emotional, relational, and financial. Among different harmful content trolling-based online content is one of them, where the idea is to post a message that is provocative, offensive, or menacing with an intent to mislead the audience. The content can be textual, visual, a combination of both, or a meme. In this study, we provide a comparative analysis of troll-based memes classification using the textual, visual, and multimodal content. We report several interesting findings in terms of code-mixed text, multimodal setting, and combining an additional dataset, which shows improvements over the majority baseline.",
}
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%0 Conference Proceedings
%T TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification
%A Nandi, Rabindra Nath
%A Alam, Firoj
%A Nakov, Preslav
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F nandi-etal-2022-teamx
%X The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social mediaplatforms, government agencies, policymakers, and society as a whole. This is because such harmful or abusive content leads to several consequences to people such as physical, emotional, relational, and financial. Among different harmful content trolling-based online content is one of them, where the idea is to post a message that is provocative, offensive, or menacing with an intent to mislead the audience. The content can be textual, visual, a combination of both, or a meme. In this study, we provide a comparative analysis of troll-based memes classification using the textual, visual, and multimodal content. We report several interesting findings in terms of code-mixed text, multimodal setting, and combining an additional dataset, which shows improvements over the majority baseline.
%R 10.18653/v1/2022.dravidianlangtech-1.13
%U https://aclanthology.org/2022.dravidianlangtech-1.13
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.13
%P 79-85
Markdown (Informal)
[TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification](https://aclanthology.org/2022.dravidianlangtech-1.13) (Nandi et al., DravidianLangTech 2022)
ACL