Content Analysis of Mediated Associations
An Automated Text-Analytic Approach
Bibliographic Data
| ID | 12971107 |
|---|---|
| Authors | Florian Arendt (0000-0003-1107-8682, Ludwig-Maximilians-Universität München, corresponding author), Narin Karadas (Ludwig-Maximilians-Universität München) |
| Year | 2017 |
| Volume | 11 |
| Issue | 2 |
| Pages | 105-120 |
| Publication date | 2017-01-20 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Communication Methods and Measures (JOURNAL) |
| Journal identifiers | ISSN: 1931-2458 • E-ISSN: 1931-2466 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/19312458.2016.1276894 |
| OpenAlex | W2571753801 |
| Language | EN |
| Citations received | 11 |
| References cited | 32 |
Due to the fact that mediated associations are a central aspect of many mass communication theories, their measurement is of central interest for communication research. Mediated associations are defined as the repeated pairing of an object (e.g., social group, political party) with specific attributes (e.g., crime, economy). In this article, we introduce a recently developed, automated text-analytic technique. We present an application of this method in the media stereotyping domain via the content analysis of German news coverage of Islam. As predicted, the analysis revealed substantial mediated associations between Islam-related concepts and violence (e.g., “Koran + violence”), terror (e.g., “Islam + terror”), dehumanization (e.g., “Muslims + animal-related terms”), and general negativity (valence). We discuss the promises and pitfalls of this method, make software suggestions, and provide application-related information for speedy dissemination in communication research
Dehumanization · German · Islam · Linguistics · Political science · Social media · Valence (chemistry · World Wide Web · Computational and Text Analysis Methods · Computer Science · Law · Media Studies and Communication · Psychology · Social Media and Politics · Social Psychology
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| Unique citing works | 11 |
|---|---|
| Citations per year | 1,38 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 10 |