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

Cho, Won Ik; Kim, Soomin

doi:10.18653/v1/2021.wnut-1.7

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Likewise, research takes perspectives on how language and linguistic variations, such as leetspeak, are used to avoid censorship through creating noisy text (Cho & Kim, 2021), or how users substitute emojis for toxic language to evade algorithmic detection of problematic content (J. Kim et al, 2022).…”

Section: Background and Related Workmentioning

confidence: 99%

You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok

Steen,

Yurechko,

Klug

2023

Social Media + Society

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Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.

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Section: Background and Related Workmentioning

confidence: 99%