A Dataset for Troll Classification of TamilMemes

Shardul Suryawanshi, Bharathi Raja Chakravarthi, Pranav Verma, Mihael Arcan, John Philip McCrae, Paul Buitelaar


Abstract
Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.
Anthology ID:
2020.wildre-1.2
Volume:
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | WILDRE | WS
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
7–13
Language:
English
URL:
https://www.aclweb.org/anthology/2020.wildre-1.2
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.wildre-1.2.pdf