Publication | Open Access
Linking Media Content and Survey Data in a Dynamic and Digital Media Environment – Mobile Longitudinal Linkage Analysis
25
Citations
27
References
2021
Year
EngineeringCommunicationMedia StudiesJournalismMedia SystemsComputational Social ScienceSurvey (Human Research)Social MediaSurvey DataMedia EffectsNews AnalyticsPolitical CommunicationSocial Medium NewsContent AnalysisStatisticsMobile Social NetworkBehavioral SciencesMedia ContentArtsUser-generated ContentMedia DistributionDigital MediaDigital News ConsumptionSocial ComputingWeb Survey MethodSocial Medium DataMedia ConsumptionLinkage AnalysisSurvey Methodology
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.
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