A Dataset for Troll Classification of Tamil Memes
If you are using the data or code from this research then please site our paper below:
@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.",
}
Data can be accessed from https://zenodo.org/record/4765573#.YKDr0SYo_0M or by mailing authors bharathiraja.akr@gmail.com and sharduls055@gmail.com.
#If you want to know more about the shared task participants and codes then please visit https://www.aclweb.org/anthology/volumes/2021.dravidianlangtech-1/