First- and second-level agenda setting in the Twittersphere
An application to the Italian political debate
Bibliographic Data
| ID | 12972126 |
|---|---|
| Authors | Andrea Ceron (0000-0002-6686-5969, Russian State Agrarian Correspondence University, corresponding author), Luigi Curini (0000-0003-1974-9815), M Iacu (0000-0002-4884-0047), Stefano M Iacus |
| Year | 2016 |
| Volume | 13 |
| Issue | 2 |
| Pages | 159-174 |
| Publication date | 2016-03-17 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Information Technology & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1933-169X • E-ISSN: 1933-1681 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/19331681.2016.1160266 |
| OpenAlex | W2299069766 |
| Language | EN |
| Citations received | 40 |
| References cited | 67 |
The rise of social network sites reopened the debate on the ability of traditional media to influence public opinion and act as an agenda setter. To answer this question, the present paper investigates first-level and second-level agenda-setting effects in the online environment by focusing on two heated Italian political debates (the reform of public funding of parties and the debate over austerity). By employing innovative and efficient statistical methods such as the lead–lag analysis and supervised sentiment analysis, we compare the attention devoted to each issue and the content spread by online news media and Twitter users. Our results show that online media keep their first-level agenda-setting power even though we find a marked difference between the slant of online news and the Twitter sentiment
Austerity · Media studies · Microblogging · Political science · Politics · Power (physics · Public opinion · Public relations · Sentiment analysis · Setter · Social media · Sociology · Computer Science · History · Law · Media Influence and Politics · Media Studies and Communication · Social Media and Politics · Artificial Intelligence
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| Unique citing works | 40 |
|---|---|
| Citations per year | 4 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 40 |