Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text

Won Ik Cho, Soomin Kim


Abstract
WARNING: This article contains contents that may offend the readers. Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as ‘Peers’ and ‘Others’. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.
Anthology ID:
2021.wnut-1.7
Volume:
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
Month:
November
Year:
2021
Address:
Online
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
56–61
Language:
URL:
https://aclanthology.org/2021.wnut-1.7
DOI:
10.18653/v1/2021.wnut-1.7
Bibkey:
Cite (ACL):
Won Ik Cho and Soomin Kim. 2021. Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 56–61, Online. Association for Computational Linguistics.
Cite (Informal):
Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text (Cho & Kim, WNUT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wnut-1.7.pdf