Road to micro-celebration
The role of mutation strategy of micro-celebrity in digital media
Bibliographic Data
| ID | 12656809 |
|---|---|
| Authors | Xingyu Chen (0000-0002-3813-6080, Shenzhen University), Ling Jiang (0000-0002-8485-8893, York University, corresponding author), Sentao Miao (0000-0002-0380-0797, McGill University), Cong Shi (0000-0003-3564-3391, University of Michigan) |
| Year | 2021 |
| Volume | 25 |
| Issue | 12 |
| Pages | 3455-3476 |
| Publication date | 2021-09-23 |
| 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 | 10.1177/14614448211045664 |
| OpenAlex | W3202141620 |
| Language | EN |
| Citations received | 6 |
| References cited | 40 |
The process of how ordinary people evolve to be well-known by delivering varied digital media content (i.e. micro-celebrification) remains perplexing. This study examines the role of mutation strategy featuring: (1) mutation diversity (the degree of evenness of content distribution across mutated styles) and (2) mutation divergence (i.e. the degree of inhomogeneity among mutated content styles), in predicting the success of micro-celebrification for ordinary people with varying talent levels. The results of survival analysis of a talent competition streamed on a major digital media platform in China suggest that a more diverse mutation in media content yields a higher chance of micro-celebrity success among participants in the competition. Interestingly, less talented participants benefit more from increasing mutation diversity compared with highly talented peers. Moreover, higher mutation divergence in the emotion evoked by media content increases the chance of success in micro-celebrification, opposite to that in the content genre and creator trait
Advertising · Biology · Business · Competition (biology · Content (measure theory · Digital content · Divergence (linguistics · Diversity (politics · Gene · Multimedia · Mutation · Selection (genetic algorithm · Sociology · Trait · Computer Science · Digital Games and Media · Digital Marketing and Social Media · Mathematics · Media Influence and Health · Psychology · Artificial Intelligence · Ecology · Genetics
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| Unique citing works | 6 |
|---|---|
| Citations per year | 2 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |