Linking Media Content and Survey Data in a Dynamic and Digital Media Environment - Mobile Longitudinal Linkage Analysis

We introduce a design that is able to face some of the challenges that digital news consumption is posing to traditional media effects methods like linkage analysis. The challenges include (1) memory errors and biases when reporting everyday news media consumption leading to (2) errors when linking...

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Veröffentlicht in:Digital journalism 2022-01, Vol.10 (1), p.200-215
Hauptverfasser: Otto, Lukas P., Thomas, Fabian, Glogger, Isabella, De Vreese, Claes H.
Format: Artikel
Sprache:eng
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Zusammenfassung:We introduce a design that is able to face some of the challenges that digital news consumption is posing to traditional media effects methods like linkage analysis. The challenges include (1) memory errors and biases when reporting everyday news media consumption leading to (2) errors when linking mass media outlets to survey data; (3) personalization of media content, as well as (4) short-term dynamic processes. Mobile Intensive Longitudinal Linkage Analysis (MILLA) uses an innovative combination of smartphone data donations to capture media exposure and relevant media content, a mobile experience sampling questionnaire to capture immediate reactions to news, and the content analysis of uploaded news media content to measure media effects. The design is explained by using an example of negativity in the news and its effects on emotional reactions of recipients.
ISSN:2167-0811
2167-082X
2167-082X
DOI:10.1080/21670811.2021.1890169