Each meme has been categorized into either a “troll” or a “not_troll” class. Along with the meme images, we also provided the Latin transcripted text from memes. We received 10 system submissions from the participants which were evaluated using the weighted average F1-score. The system with the weighted average F1-score of 0.55 secured the first rank.
Language - Tamil
Authors - Suryawanshi, Shardul and Chakravarthi, Bharathi Raja and Verma, Pranav and Arcan, Mihael and McCrae, John P and Buitelaar, Paul
Reference -https://zenodo.org/record/4765573#.YhumaOhBxEZ
Citation - Creative Commons Attribution 4.0 International@inproceedings{suryawanshi-etal-2020-tamil-meme,
title = "A Dataset for Troll Classification of {Tamil} Memes",
author = "Suryawanshi, Shardul and
Chakravarthi, Bharathi Raja and
Verma, Pranav and
Arcan, Mihael and
McCrae, John P and
Buitelaar, Paul",
booktitle = "Proceedings of the 5th Workshop on Indian Language Data Resource and
Evaluation (WILDRE-5)",
month = May,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)"
}
@inproceedings{suryawanshi-chakravarthi-2021-findings,
title = "Findings of the Shared Task on Troll Meme Classification in {T}amil",
author = "Suryawanshi, Shardul and
Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.16",
pages = "126--132",
abstract = "The internet has facilitated its user-base with a platform to communicate
and express their views without any censorship. On the other hand, this freedom of expression or free speech
can be abused by its user or a troll to demean an individual or a group. Demeaning people based on their
gender, sexual orientation, religious believes or any other characteristics {--}trolling{--} could cause
great distress in the online community. Hence, the content posted by a troll needs to be identified and
dealt with before causing any more damage. Amongst all the forms of troll content, memes are most
prevalent due to their popularity and ability to propagate across cultures. A troll uses a meme to
demean, attack or offend its targetted audience. In this shared task, we provide a resource (TamilMemes)
that could be used to train a system capable of identifying a troll meme in the Tamil language.
In our TamilMemes dataset, each meme has been categorized into either a
{``}troll{''} or a {``}not{\_}troll{''} class. Along with the meme images, we also provided
the Latin transcripted text from memes. We received 10 system submissions from the participants
which were evaluated using the weighted average F1-score.
The system with the weighted average F1-score of 0.55 secured the first rank.",
}
