Testing Visual Signals in Representative Surveys in Combination with Media Content Analyses of the 2002 German Federal Election Campaign
Bibliographic Data
| ID | 12232802 |
|---|---|
| Authors | T Petersen, Thomas Søbirk Petersen (0000-0002-1955-8165, corresponding author), Olaf Jandura (0000-0001-6838-0327) |
| Year | 2006 |
| Volume | 19 |
| Issue | 1 |
| Pages | 89-96 |
| Publication date | 2006-03-13 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Public Opinion Research (JOURNAL) |
| Journal identifiers | ISSN: 0954-2892 • E-ISSN: 1471-6909 |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1093/ijpor/edl014 |
| OpenAlex | W2104687929 |
| Language | EN |
| Citations received | 1 |
| References cited | 1 |
Surveys ’ (Petersen, 2005), which described a new research concept that combines survey research and quantitative media content analysis in an attempt to define individual ele-ments or ‘signals ’ in visual news coverage and to quantitatively gauge the strength of their effect, ultimately allowing these signals to be incorporated in applied quantitative media effects research. Via a number of similarly designed individual studies, the approach aims, in the long run, to provide an overview or create a ‘map, ’ so to speak, of the effect of various visual signals and their relative strength—a map that could someday enable us to meet the challenge already outlined by Harold Lasswell in 1942, that is to find a way to compare the strength of the effect exerted by an illustration included in media reporting, depending on its content and ‘signal value, ’ with a certain quantity of textual reporting (Lasswell, 1942, p. 14). The research concept comprises three elements. First of all, it is necessary to define visual elements that are commonly found in media reporting and that are easily recogniz-able and classifiable. The next step is to test the effect of these visual attributes by pre-senting them to respondents in representative surveys of the population. The most
Content (measure theory · Content analysis · Data science · Econometrics · Geography · German · Machine learning · Media content · Media coverage · Media studies · Multimedia · Political science · Social science · Sociology · Statistics · Value (mathematics · Computer Science · Mathematics · Public Spaces through Art
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,07 |
| Citation span | 2011 - 2011 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |