Open AccessSimulating hidden dynamics
Author(s) -
Martin Wettstein
Publication year - 2020
Publication title -
computational communication research
Language(s) - English
Resource type - Journals
ISSN - 2665-9085
DOI - 10.5117/ccr2020.1.001.wett
Subject(s) - linkage (software) , explanatory power , computer science , field (mathematics) , referendum , econometrics , computation , process (computing) , aggregate (composite) , media content , regression analysis , data science , empirical research , data mining , machine learning , algorithm , statistics , mathematics , political science , philosophy , biochemistry , chemistry , multimedia , politics , materials science , epistemology , pure mathematics , law , composite material , gene , operating system
Linkage analyses use data from panel surveys and content analyses to assess media effects under field conditions and are able to close the gap between experimental and survey-based media effects research. Results from current studies and simulations indicate, however, that these studies systematically under-estimate real media effects as they aggregate measurement errors and reduce the complexity of media content. In response to these issues, we propose a new method for linkage analysis which applies agent-based simulations to directly assess short-term media effects using empirical data as guideposts. Results from an example study modeling opinion dynamics in the run-up of a Swiss referendum show that this method outperforms traditional regression-based linkage analyses in detail and explanatory power. In spite of the time-consuming modeling and computation process, this approach is a promising tool to study individual media effects under field conditions.