Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.06376 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908666105430016 |
|---|---|
| author | McCutcheon, Austin Brogly, Chris |
| author_facet | McCutcheon, Austin Brogly, Chris |
| contents | Clickbait is deceptive text that can manipulate web browsing, creating an information gap between a link and target page that literally baits a user into clicking. Clickbait detection continues to be well studied, but analyses of clickbait overall on the web are limited. A dataset was built consisting of 451,033,388 clickbait scores produced by a clickbait detector which analyzed links and headings on primarily English news pages from the Common Crawl. On this data, 5 segmented regression models were fit on 5 major news events and averaged clickbait scores. COVID and the 2020 US Election appeared to influence clickbait levels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interrupted time series analysis of clickbait on worldwide news websites, 2016-2023 McCutcheon, Austin Brogly, Chris Social and Information Networks Clickbait is deceptive text that can manipulate web browsing, creating an information gap between a link and target page that literally baits a user into clicking. Clickbait detection continues to be well studied, but analyses of clickbait overall on the web are limited. A dataset was built consisting of 451,033,388 clickbait scores produced by a clickbait detector which analyzed links and headings on primarily English news pages from the Common Crawl. On this data, 5 segmented regression models were fit on 5 major news events and averaged clickbait scores. COVID and the 2020 US Election appeared to influence clickbait levels. |
| title | Interrupted time series analysis of clickbait on worldwide news websites, 2016-2023 |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2408.06376 |