Name
Abstract | ||
|---|---|---|
The present work proposes social media as a tool to understand the relationship between journalists' social networks and the content they produce. Specifically, we ask, what is the association between the partisan nature of the accounts journalists follow on Twitter and the news content they produce? Using standard text scaling techniques, we analyze partisanship in a novel dataset of more than 300,000 news articles produced by 644 journalists at 25 different US news outlets. We then develop a novel, semi-supervised model of partisanship of Twitter following relationships and show a modest correlation between the partisanship of whom a journalist follows on Twitter and the content she produces. The findings provide insight into the partisan dynamics that appear to characterize the US media ecosystem in its broad contours, dynamics that may be traceable from social media networks to published stories. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/1461444818807133 | NEW MEDIA & SOCIETY |
Keywords | Field | DocType |
Computational social science,journalism,partisanship,social media,text analysis,Twitter | Social science,Social media,Social network,Journalism,Media studies,Sociology,News media,Computational sociology | Journal |
Volume | Issue | ISSN |
21.0 | 4 | 1461-4448 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Wihbey | 1 | 2 | 2.05 |
| Kenneth Joseph | 2 | 70 | 9.46 |
| David Lazer | 3 | 290 | 24.46 |