Simulating hidden dynamics | Amsterdam University Press Journals Online
2004
Volume 2, Issue 1
  • E-ISSN: 2665-9085

Abstract

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.5117/CCR2020.1.001.WETT
2020-02-01
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/26659085/2/1/01_CCR2020.1.001.WETT.html?itemId=/content/journals/10.5117/CCR2020.1.001.WETT&mimeType=html&fmt=ahah

References

  1. Bartlett, P. L., Boucheron, S., & Lugosi, G.(2002). Model Selection and Error Estimation.Machine Learning, 48(1/3), 85–113. doi:10.1023/A:1013999503812
    [Google Scholar]
  2. Bousquet, F., Cambier, C., Mullon, C., Morand, P., & Quensiere, J.(1995). Sumulating Fishermen’s Society. In N.Gilbert & J.Doran (Eds.), Simulating societies: The computer simulation of social phenomena (2nd ed., pp. 143–164). London: UCL Press.
    [Google Scholar]
  3. Charbonneau, P.(2002). An Introduction to Genetic Algorithms for Numerical Optimization.Advance online publication. doi:10.5065/D608638S
    [Google Scholar]
  4. Chmiel, A., Sobkowicz, P., Sienkiewicz, J., Paltoglou, G., Buckley, K., Thelwall, M., & Hołyst, J. A.(2011). Negative emotions boost user activity at BBC forum.Physica a: Statistical Mechanics and Its Applications, 390(16), 2936–2944. doi:10.1016/j.physa.2011.03.040
    [Google Scholar]
  5. Dorigo, M.(1992). Optimization, Learning and Natural Algorithms. Milano, IT: PhD Thesis, Politecnico di Milano.
    [Google Scholar]
  6. Efron, B.(1981). Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods.Biometrika, 68(3), 589–599. doi:10.1093/biomet/68.3.589
    [Google Scholar]
  7. Gilbert, N., & Troitzsch, K. G.(2005). Simulation for the social scientist. Maidenhead, UK: Open University Press.
    [Google Scholar]
  8. Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H.(2018). Computational modelling of public policy: Reflections on practice.Journal of Artificial Societies and Social Simulation, 21(1), 14. doi:10.18564/jasss.3669
    [Google Scholar]
  9. Glover, F.(1986). Future paths for integer programming and links to artificial intelligence.Computers & Operations Research, 13(5), 533–549. https://doi.org/10.1016/0305-0548(86)90048-1
    [Google Scholar]
  10. Hayes, A. F., & Scharkow, M.(2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter?Psychological Science, 24(10), 1918–1927. doi:10.1177/0956797613480187
    [Google Scholar]
  11. Kepplinger, H. M., Brosius, H.-B., & Staab, J. F.(1991). Opinion Formation in Mediated Conflicts and Crises: A Theory of Cognitive-Affective Media Effects.International Journal of Public Opinion Research, 3(2), 132–156. doi:10.1093/ijpor/3.2.132
    [Google Scholar]
  12. Koskinen, J. H. & Snijders, A. B.(2007). Bayesian inference for dynamic social network data.Journal of Statistical Planning and Interference13, 3930-3938.
    [Google Scholar]
  13. Latané, B.(1981). The Psychology of Social Impact.American Psychologist, 36(4), 343–356.
    [Google Scholar]
  14. Macy, M. W., & Willer, R.(2002). From Factors to Actors: Computational Sociology and Agent-Based Modeling.Annual Review of Sociology, 28(1), 143–166. doi:10.1146/annurev.soc.28.110601.141117
    [Google Scholar]
  15. Matthes, J.(2012). Exposure to Counterattitudinal News Coverage and the Timing of Voting Decisions.Communication Research, 39(2), 147–169. doi:10.1177/0093650211402322
    [Google Scholar]
  16. Miller, A. H., Goldenberg, E. N., & Erbring, L.(1979). Type-Set Politics: Impact of Newspapers on Public Confidence.American Political Science Review, 73(01), 67–84. doi:10.2307/1954731
    [Google Scholar]
  17. Muis, J.(2010). Simulating political stability and change in the Netherlands (1998-2002): An agent-based model of party competition with media effects empirically tested.Journal of Artificial Societies and Social Simulation, 13(13), 4. doi:10.18564/jasss.1482
    [Google Scholar]
  18. Muis, J., & Scholte, M.(2013). How to find the ‘winning formula’? Conducting simulation experiments to grasp the tactical moves and fortunes of populist radical right parties.Acta Politica, 48(1), 22-46. doi:10.1057/ap.2012.21
    [Google Scholar]
  19. Noelle-Neumann, E.(1974). The Spiral of Silence: A Theory of Public Opinion.Journal of Communication, 24(2), 43–51.
    [Google Scholar]
  20. Nowak, A., & Latané, B.(1995). Simulating the emergence of social order from individual behavior. In N.Gilbert & J.Doran (Eds.), Simulating societies: The computer simulation of social phenomena (2nd ed., pp. 63–84). London: UCL Press.
    [Google Scholar]
  21. Nowak, A., Szamrej, J., & Latané, B.(1990). Prom Private Attitude to Public Opinion: A Dynamic Theory of Social Impact.Psychological Review, 97(3), 362–376.
    [Google Scholar]
  22. Peter, J.(2003). Country Characteristics as Contingent Conditions of Agenda Setting.Communication Research, 30(6), 683–712. doi:10.1177/0093650203257844
    [Google Scholar]
  23. Railsback, S. F., & Grimm, V.(2012). Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton University Press.
    [Google Scholar]
  24. Scharkow, M., & Bachl, M.(2016). How Measurement Error in Content Analysis and Self-Reported Media Use Leads to Minimal Media Effect Findings in Linkage Analyses: A Simulation Study.Political Communication, 9(4), 1–21. doi:10.1080/10584609.2016.1235640
    [Google Scholar]
  25. Schemer, C.(2014). Media Effects on Racial Attitudes: Evidence from a Three-Wave Panel Survey in a Political Campaign.International Journal of Public Opinion Research, 26(4), 531–542. doi:10.1093/ijpor/edt041
    [Google Scholar]
  26. Schemer, C., Wirth, W., & Matthes, J.(2012). Value Resonance and Value Framing Effects on Voting Intentions in Direct-Democratic Campaigns.American Behavioral Scientist, 56(3), 334–352. doi:10.1177/0002764211426329
    [Google Scholar]
  27. Scheufele, B.(2008). The dilemma of media effects research. A logic for modeling media effects on meso- and macro-level units both in theoretical und methodical respect [German].Publizistik, 53(3), 339–361. doi:10.1007/PL00022227
    [Google Scholar]
  28. Schuck, A. R. T., Boomgaarden, H. G., & Vreese, C. H. de.(2013). Cynics All Around? The Impact of Election News on Political Cynicism in Comparative Perspective.Journal of Communication, 63(2), 287–311. doi:10.1111/jcom.12023
    [Google Scholar]
  29. Snijders, T. A. B. (2017a). Stochastic Actor-Oriented Models for Network Dynamics.Annual Review of Statistics and Its Application, 4(1), 343–363. doi:10.1146/annurev‑statistics‑060116‑054035
    [Google Scholar]
  30. Snijders, T. A. B. (2017b). Siena Algorithms. Retrieved from https://www.stats.ox.ac.uk/~snijders/siena/Siena_algorithms.pdf
  31. Van Laarhoven, P. J. M., & Aarts, E. H. L.(1987). Simulated annealing. In P. J. M.van Laarhoven & E. H. L.Aarts (Eds.), Mathematics and its applications: Vol. 37. Simulated annealing: Theory and applications (1st ed., pp. 7–15). Dordrecht: Springer. doi:10.1007/978‑94‑015‑7744‑1_2
    [Google Scholar]
  32. Voinov, A., Kolagani, N., McCall, M. K., Glynn, P. D., Kragt, M. E., Ostermann, F. O., Ramu, P.(2016). Modelling with stakeholders – Next generation.Environmental Modelling & Software, 77, 196–220. doi:10.1016/j.envsoft.2015.11.016
    [Google Scholar]
  33. Waldherr, A.(2014). Emergence of news waves: A social simulation approach.Journal of Communication, 64(5), 852-873. doi:10.1111/jcom.12117
    [Google Scholar]
  34. Waldherr, A. & Wettstein, M.(2019). Bridging the Gaps. Using Agent-Based Modeling to Reconcile Data and Theory in Computational Communication Science.International Journal of Communication13, 3976-3999.
    [Google Scholar]
  35. Wettstein, M.(2012). Frame Adoption in Referendum Campaigns: The Effect of News Coverage on the Public Salience of Issue Interpretations.American Behavioral Scientist, 56(3), 318–333. doi:10.1177/0002764211426328
    [Google Scholar]
  36. Wirth, W., Matthes, J., Schemer, C., Wettstein, M., Friemel, T., Hänggli, R., & Siegert, G.(2010). Agenda Building and Setting in a Referendum Campaign: Investigating the Flow of Arguments Among Campaigners, the Media, and the Public.Journalism & Mass Communication Quarterly, 87(2), 328–345.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.5117/CCR2020.1.001.WETT
Loading
/content/journals/10.5117/CCR2020.1.001.WETT
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Agent-Based Modeling; linkage analysis; simulation
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error