Moving up. Applying aggregate level time series analysis in the study of media coverage

Rens Vliegenthart*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

Abstract

In this article the advantages of aggregate level time series analysis for the study of media coverage are discussed. This type of analysis offers the opportunity to answer questions relating to causes and effects of media attention for issues and all kind of other content characteristics. Data that ask for a time series approach have become widely available during the past years, due to the rise of digital archives and social media such as Twitter and Facebook. This type of analysis allows for answering a set of interesting research questions and strong inferences about causal processes. Common challenges in time series analysis, relating to stationarity, accounting for a series' past and autoregressive conditional heteroscedasticity are discussed. Two useful approaches, ARIMA and VAR, are introduced stepwise. An empirical example, dealing with intermedia agenda-setting between different newspapers in the Netherlands, demonstrates how both techniques can be applied and how they provide insightful answers to interesting research problems.

Original languageEnglish
Pages (from-to)2427-2445
Number of pages19
JournalQuality and Quantity
Volume48
Issue number5
DOIs
Publication statusPublished - Sep 2014
Externally publishedYes

Keywords

  • ARIMA
  • Intermedia agenda-setting
  • Media coverage
  • Time series analysis
  • VAR

Fingerprint

Dive into the research topics of 'Moving up. Applying aggregate level time series analysis in the study of media coverage'. Together they form a unique fingerprint.

Cite this