Corporate censorship online
Vagueness and discursive imprecision in YouTube’s advertiser-friendly content guidelines
Bibliographic Data
| ID | 12656356 |
|---|---|
| Authors | Susanne Kopf (0000-0002-1566-9300, Vienna University of Economics and Business, corresponding author) |
| Year | 2022 |
| Volume | 26 |
| Issue | 4 |
| Pages | 1756-1774 |
| Publication date | 2022-02-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | New Media & Society (JOURNAL) |
| Journal identifiers | ISSN: 1461-4448 • E-ISSN: 1461-7315 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/14614448221077354 |
| OpenAlex | W4211067817 |
| Language | EN |
| Citations received | 10 |
| References cited | 34 |
This article deals with YouTube’s advertiser-friendly content guidelines – the content rules dedicated to defining what YouTube deems advertiser (un)friendly and that YouTube creators seeking to monetise their content through advertising have to follow. Specifically, this study addresses the textual composition of YouTube’s regulations with a focus on occurrences of vagueness. Regarding methods of data analysis, I take a corpus-assisted discourse analytical approach to YouTube’s texts on advertiser-friendliness. That is, I take a wide-angle view on all concordance lines of ‘content’ and identify occurrences of different forms of vagueness. Findings suggest that at least 26% of the lines of ‘content’ detailing YouTube’s ad-friendly content guidelines exhibit at least one of eight forms of vagueness. Consequently, content creators – left unsure about the monetisability of particular content – may choose not to push any boundaries but to produce noncontroversial content, which, in turn, may impede content plurality on YouTube
Advertising · Business · Censorship · Content (measure theory · Content analysis · Focus (optics · Fuzzy logic · Internet privacy · Political science · Sociology · Vagueness · Computer Science · Gender, Feminism, and Media · Hate Speech and Cyberbullying Detection · Law · Mathematics · Social Media and Politics · Artificial Intelligence
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| Unique citing works | 10 |
|---|---|
| Citations per year | 3,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 10 |