, Volume 63, Issue 4, pp 557–582 | Cite as

Der Wert der Worte – Wie digitale Methoden helfen, Kommunikations- und Wirtschaftswissenschaft zu verknüpfen

  • Henrik MüllerEmail author
  • Gerret von Nordheim
  • Karin Boczek
  • Lars Koppers
  • Jörg Rahnenführer


Die Anwendung digitaler Methoden bietet die Chance, Kommunikationswissenschaften und Ökonomik enger zu verknüpfen. So besteht in der Konjunktur- und der Finanzmarktforschung seit einigen Jahren ein wachsendes Interesse an der Einbeziehung von Medieninhalten, allerdings meist ohne Rückgriff auf die Kommunikationswissenschaften. Dieser Beitrag gibt einen Überblick über Ansätze zur Einbeziehung von Medieninhalten in der Wirtschaftsforschung und stellt einen neuen, multidimensionalen Indikator vor, der wirtschaftspolitische Unsicherheit auf der Grundlage des Topic-Modelling-Verfahrens Latent Dirichlet Allocation (LDA) misst: den Uncertainty Perception Indicator (UPI). Auf dieser Basis zeigen wir, wie sich der in der Ökonomik populäre Begriff des „Narrativs“ operationalisieren und mit dem kommunikationswissenschaftlichen Konzept des Frames verknüpfen lässt.


Topic Modelling Wirtschaftswissenschaften Narrative Framing Unsicherheit 

The value of words—how digital methods help to connect communication science and economics


The use of digital methods offers a chance to connect communication science with economics. In recent years, a growing body of research in economics has turned its attention to media content, assuming that journalistic coverage contains hitherto neglected information relevant for business cycles or financial market movements. Interestingly, these approaches largely ignore communication science’s established theories and empirical findings. This paper aims at building a bridge between the two disciplines. Its contribution is threefold: a) it provides an overview of the most important approaches in economics that incorporate media content; b) it operationalizes the concept of the “narrative”, as it is used in economics, and distinguishes it from the concept of the “frame”, essential in communication science; c) exemplifying our approach, we present a new Uncertainty Perception Indicator (UPI) based on the topic modeling method Latent Dirichlet Allocation (LDA), that enables us to isolate different factors of economic policy uncertainty contained in media coverage.

Economic studies treat journalistic media content as a proxy for sentiment prevalent in society. Typically, they rely on frequency analyses of certain keywords, like “recession” or “inflation”. Even more sophisticated approaches, such as Shiller (2017), who calls for establishing a new branch of “narrative economics”, or Baker et al. (2016), who construct a comprehensive set of media-based indicators, make no or little reference to communication science. This neglect could be discounted as pure ignorance, but this misses the point. Being a predominantly empirical discipline today, economics relies on long time-series of data, that have not been available for media content, a gap rendering the two disciplines largely incompatible.

The gap is also reflected in terminology. “Frame” is a major analytical concept in communication science, while the term “narrative” has become in vogue in economics. Although both concepts are closely connected, they are rarely properly distinguished from each other. “Frame” can be considered as a rather static concept that applies during a limited period of time. “Narrative”, in contrast, implies dynamic properties, i. e., the sorting of events, causes and effects over time, that explain how the current state of the world has come about, as stressed by Tenenboim-Weinblatt et al. (2016).

In this paper, we propose a synergetic concept. Following Entman (1993), a media frame contains four elements: a) a problem definition, b) a problem diagnosis, c) a moral judgement, and d) possible remedies. We augment this approach by adding two more elements. According to our definition, a media narrative comprises a frame, or several ones, plus e) one or several protagonists—persons, institutions, or social groupings (nations, classes, etc.)—, whose relationships are (often) antagonistic and may change over time; and f) events, that are chronologically integrated and that are (often) assumed to constitute causal relationships. To put it metaphorically: a frame is to a narrative what a still photo is to a movie. Both are valuable concepts; the still photo shows more details, while the movie provides a contextualization over time.

Topic models like LDA are valuable tools for the measurement of media narratives. The probabilistic approach enables researchers to conduct what may be called “macro-content analyses”, an exercise that focuses on average reporting patterns in large text corpora and can be translated into numerical time-series, thereby facilitating compatibility with empirical economics. Based on a topic’s frequency analysis, its top words and top articles, “mean media narratives” can be formulated, that integrate certain events, protagonists and frames.

In our case study, we exemplify this concept by applying it to an indicator that is currently popular in economics, the Economic Policy Uncertainty Index (Baker et al. 2016). The EPU aims at capturing political developments that are exogenous to economic models and therefore unpredictable. Essentially, the indicator is based on the counts of articles containing a set of search words, such as “uncertain”, “economic” as well as institutions like the European Central Bank. Using identical search words as the EPU for Germany, we construct a similar corpus for the years 1994 to 2017. By conducting an LDA-based analysis, we are able to extract additional relevant information from the data. In particular, the evolution of different uncertainty factors and their development over time can be detected.

Our Uncertainty Perception Indicator (UPI) contains six relevant news topics that are highly relevant for market developments: central banks, the national government, international politics, the business cycle, companies, and society. While the EPU merely shows how often uncertainty concerning economic policy is mentioned in the media, the UPI also indicates the origins of uncertainty. By grouping the six topics into three analytical categories—governments, markets, and society—we find a distinct break in the time-series. Before the financial crisis of 2008, the perception of uncertainty was rather balanced between the three factors. Since then, however, economic uncertainty has mainly been driven by political actors, most prominently by central banks. The corresponding narratives are a two-chapter story: in the first part, up to 2008, stable financial markets and smoothed business cycles prevailed, making central banking a rather straight-forward task. The second part is characterized by multiple crises, leaving central banks as dominant actors, that intervened with unconventional measures. Thereby, they became stabilizing forces, but at the same time sources of uncertainty with respect to the timing and the impact of these measures.


Topic models Economics Narrative Framing Uncertainty 



Die Autoren danken den Gutachtern und Herausgebern für viele wertvolle Hinweise, die Eingang in dieses Papier gefunden haben.


  1. Akerlof, G. A., & Shiller, R. J. (2009). Animal spirits. How human psychology drives the economy, and why it matters for global capitalism. Princeton: Princeton University Press.Google Scholar
  2. Akerlof, G. A., & Shiller, R. J. (2015). Phishing for fools. The economics of manipulation and deception. Princeton: Princeton University Press.CrossRefGoogle Scholar
  3. Akerlof, G. A., & Snower, D. J. (2016). Bread and bullets. Kiel working paper no. 2022.Google Scholar
  4. Ammann, M., Frey, R., & Verhofen, M. (2012). Do newspaper articles predict aggregate stock returns? Working papers on finance no. 2012/4. St. Gallen: Swiss Institute of Banking and Finance.Google Scholar
  5. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131, 1593–1636.CrossRefGoogle Scholar
  6. Bird, E., & Dardenne, R. W. (2009). Rethinking news and myth as storytelling. In T. Hanitzsch & K. Wahl-Jorgensen (Hrsg.), The handbook of journalism studies (S. 205–217). New York: Routledge.Google Scholar
  7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. Scholar
  8. Boumans, J. W., & Trilling, D. (2016). Taking stock of the toolkit: an overview of relevant autmated content analysis approaches and techniques for digital journalism scholars. Digital Journalism, 4(1), 8–23.CrossRefGoogle Scholar
  9. Brosius, H.-B., & Weimann, G. (1996). Who sets the agenda: agenda-setting as a two-step flow. Communication Research, 23, 561–580.CrossRefGoogle Scholar
  10. Brunnermeier, M. K., James, H., & Landau, J.-P. (2016). The Euro and the battle of ideas. Princeton: Princeton University Press.Google Scholar
  11. Burscher, B., Odijk, D., Vliegenthart, R., de Rijke, M., & de Vreese, C. H. (2014). Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis. Communication Methods and Measures, 8(3), 190–206.CrossRefGoogle Scholar
  12. Bybee, J. L., & Hopper, P. J. (2001). Introduction to frequency and the emergence of linguistic structure. In J. L. Bybee & P. J. Hopper (Hrsg.), Frequency and the emergence of linguistic structure. Amsterdam: John Benjamins.CrossRefGoogle Scholar
  13. Calomiris, C. W., & Mamaysky, H. (2018). How news and its context drive risk and returns around the world. NBER working paper 24430.CrossRefGoogle Scholar
  14. Carroll, C. D. (2003). Macroeconomic expectations of households and professional forecasters. The Quarterly Journal of Economics, 118(1), 269–298.CrossRefGoogle Scholar
  15. Cohen, B. (1963). The press and foreign policy. New York: Harcourt.Google Scholar
  16. Czarniawska, B. (2004). Narratives in social science research. London: SAGE.CrossRefGoogle Scholar
  17. De Boef, S., & Kellstedt, P. M. (2004). The political (and economic) origins of consumer confidence. American Journal of Political Science, 48, 633–649.CrossRefGoogle Scholar
  18. De Vreese, C. H. (2010). Framing the economy: effects of journalistic news frames. In P. D’Angelo & J. Kuypers (Hrsg.), Doing framing analysis: empirical and theoretical perspectives (S. 187–214). New York: Routledge.Google Scholar
  19. DiMaggio, P., Nag, M., & Blei, D. M. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of U.S. government arts funding. Poetics, 41, 570–606.CrossRefGoogle Scholar
  20. Doms, M., & Morin, N. (2004). Consumer sentiment, the economy, and the news media. Finance and economics discussion series 2004-51. Washington: Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board.Google Scholar
  21. Entman, R. M. (1993). Framing: toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58.CrossRefGoogle Scholar
  22. European Economic Advisory Group (EEAG) (2018). EEAG report on the European economy. München: CESifo.Google Scholar
  23. Fogarty, B. J. (2005). Determining economic news coverage. International Journal of Public Opinion Research, 17, 149–172.CrossRefGoogle Scholar
  24. Gamson, W. A., Croteau, D., Hoynes, W., & Sasson, T. (1992). Media images and the social construction of reality. Annual Review of Sociology, 18, 373–393.CrossRefGoogle Scholar
  25. Ghanem, S. I. (1997). Filling in the tapestry: the second level of agenda setting. In M. E. McCombs, D. L. Shaw & D. H. Weaver (Hrsg.), Communication and democracy (S. 3–14). Mahwah: Lawrence Erlbaum.Google Scholar
  26. Grimmer, J., & Stewart, B. M. (2013). Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21, 267–297.CrossRefGoogle Scholar
  27. Günther, E., & Quandt, T. (2016). Word counts and topic models. Digital Journalism, 4(1), 75–88.CrossRefGoogle Scholar
  28. Györffy, D. (2013). Institutional trust and economic policy. Lessons from the history of the Euro. Budapest: Central European University Press.Google Scholar
  29. Haller, H. B., & Norpoth, H. (1997). Reality bites: news exposure and economic opinion. Public Opinion Quarterly, 61, 555–576.CrossRefGoogle Scholar
  30. Harris, Z. S. (1951). Methods in structural linguistics. Chicago: University of Chicago Press.Google Scholar
  31. Hepp, A. (2016). Kommunikations- und Medienwissenschaft in datengetriebenen Zeiten. Publizistik, 61, 225–246.CrossRefGoogle Scholar
  32. Hickethier, K. (1997). Das Erzählen der Welt in den Fernsehnachrichten. Überlegungen zu einer Narrationstheorie der Nachricht. Rundfunk und Fernsehen, 45, 5–18.Google Scholar
  33. Iyengar, S., & Kinder, D. R. (1987). News that matters: television and American opinion. American politics and political economy series. Chicago: University of Chicago Press.Google Scholar
  34. Keynes, J. M. (1936). The general theory of employment, interest and money. London: Palgrave Macmillan.Google Scholar
  35. Kholodilin, K., Thomas, T., & Ulbricht, D. (2014). Do media data help to predict German industrial production? Dice discussion paper no 149.Google Scholar
  36. Kholodilin, K., Kolmer, C., Thomas, T., & Ulbrich, D. (2015). Asymmetric perceptions of the economy: media, firms, consumers, and experts. DIW discussion paper 1490.Google Scholar
  37. Knight, F. (1921). Risk, uncertainty and profit. Cambridge: The Riverside Press.Google Scholar
  38. Koppers, L., Rieger, J., Boczek, K., & von Nordheim, G. (2018). tosca (tools for statistical content analysis). Zugegriffen: 10. Oktober 2018.Google Scholar
  39. Larsen, V. H., & Thorsrud, L. A. (2015). The value of news. CAMP working paper series. No 6/2015.Google Scholar
  40. Linden, F. (1982). The consumer as forecaster. Public Opinion Quarterly, 46, 353–360.CrossRefGoogle Scholar
  41. Lischka, J. A. (2016). Economic news, sentiment, and behavior. Wiesbaden: Springer.CrossRefGoogle Scholar
  42. Lünenborg, M. (2005). Journalismus als kultureller Prozess. Zur Bedeutung von Journalismus in der Mediengesellschaft. Ein Entwurf. Wiesbaden: Springer.CrossRefGoogle Scholar
  43. Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., et al. (2018). Applying LDA topic modeling in communication research. Toward a valid and reliable methodology. Communication Methods and Measures, 54(10), 1–26.Google Scholar
  44. Matthes, J. (2006). The need for orientation towards news media: revising and validating a classic concept. International Journal of Public Opinion Research, 18, 422–444.CrossRefGoogle Scholar
  45. McComas, K., & Shanahan, J. (1999). Telling stories about global climate change. Measuring the impact of narratives on issue cycles. Communuication Research, 26, 30–57.CrossRefGoogle Scholar
  46. McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36, 176–187.CrossRefGoogle Scholar
  47. Müller, H., Porcaro, G., & von Nordheim, G. (2018). Tales from a crisis: diverging narratives of the euro area. Bruegel policy contribution, issue no. 03.Google Scholar
  48. Nadeau, R., Niemi, R. G., Fan, D. P., & Amato, T. (1999). Elite economic forecasts, economic news, mass economic judgments, and presidential approval. Journal of Politics, 61, 109–135.CrossRefGoogle Scholar
  49. Neuman, R. W., Guggenheim, L., Jang, M. S., & Bae, S. Y. (2014). The dynamics of public attention: agenda-setting theory meets big data. Journal of Communication, 64, 193–214.CrossRefGoogle Scholar
  50. Noelle-Neumann, E., & Mathes, R. (1987). The ‘event as event’ and the ‘event as news’: the significance of ‘consonance’ for media effects research. European Journal of Communication, 2, 391–414.CrossRefGoogle Scholar
  51. Projektgruppe Gemeinschaftsdiagnose (2018). Deutsche Wirtschaft im Boom – Luft wird dünner. Gemeinschaftsdiagnose 1/2018. Zugegriffen: 20. Apr. 2018.Google Scholar
  52. Puschmann, C., & Scheffler, T. (2016). Topic modeling for media and communication research. A short primer. HIIG discussion paper series 5. (S. 1–17). Google Scholar
  53. Richter, R. (2015). Essays in institutional economics. Heidelberg: Springer.CrossRefGoogle Scholar
  54. Roeh, I. (1989). Journalism as storytelling, coverage as narrative. American Behavioral Scientist, 33(2), 162–168.CrossRefGoogle Scholar
  55. Shiller, R. J. (2000). Irrational exuberance. Princeton: Princeton University Press.Google Scholar
  56. Shiller, R. J. (2017). Narrative economics. American Economic Review, 107(4), 967–1004.CrossRefGoogle Scholar
  57. Soroka, S. N. (2006). Good news and bad news: asymmetric responses to economic information. Journal of Politics, 68, 372–385.CrossRefGoogle Scholar
  58. Strippel, C., Bock, A., Katzenbach, C., Mahrt, M., Merten, L., Nuernbergk, C., Pentzold, C., Puschmann, C., & Waldherr, A. (2018). Die Zukunft der Kommunikationswissenschaft ist schon da, sie ist nur ungleich verteilt. Eine Kollektivreplik. Publizistik, 63, 11–27.CrossRefGoogle Scholar
  59. Tenenboim-Weinblatt, K., & Neiger, M. (2015). Print is future, online is past: cross-media analysis of temporal orientations in the news. Communication Research, 42, 1047–1067.CrossRefGoogle Scholar
  60. Tenenboim-Weinblatt, K., Hanitzsch, T., & Nagar, R. (2016). Beyond peace journalism: reclassifying conflict narratives in the Israeli news media. Journal of Peace Research, 53(2), 151–165.CrossRefGoogle Scholar
  61. Tetlock, P. C. (2007). Giving content to investor sentiment: the role of media in the stock market. The Journal of Finance, 62, 1139–1168.CrossRefGoogle Scholar
  62. Thorsrud, L. A. (2016a). Nowcasting using news topics. Big data versus big bank. CAMP working paper series no 6/2016.Google Scholar
  63. Thorsrud, L. A. (2016b). Words are the new numbers: a newsy coincident index of business cycles. CAMP working paper series, no 4/2016.Google Scholar
  64. Tsur, O., Calacci, D., & Lazer, D. (2015). A frame of mind: using statistical models for detection of framing and agenda setting campaigns. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (S. 1629–1638). Stroudsburg: ACL.Google Scholar
  65. Tuchman, G. (1976). Telling stories. Journal of Communication, 26(4), 93–97.CrossRefGoogle Scholar
  66. Vosen, S., & Schmidt, T. (2011). Forecasting private consumption: survey-based indicators vs. google trends. Journal of Forecasting, 30, 565–578.CrossRefGoogle Scholar
  67. Wanta, W., & Hu, Y.-W. (1994). The effects of credibility, reliance, and exposure on media agenda-setting: a path analysis model. Journalism Quarterly, 71, 90–98.CrossRefGoogle Scholar
  68. Wu, H. D., & Coleman, R. (2009). Advancing agenda-setting theory: the comparative strength and new contingent conditions of the two levels of agenda-setting effects. Journalism & Mass Communication Quarterly, 86(4), 775–789.CrossRefGoogle Scholar
  69. Wu, H. D., Stevenson, R. L., Hsiao-Chi, C., & Guner, Z. N. (2002). The conditional impact of recession news: a time-series analysis of economic communication in the United States, 1987–1996. International Journal of Public Opinion Research, 14, 19–36.CrossRefGoogle Scholar
  70. Wyss, V. (2010). Narration freilegen: Zur Konsequenz der Mehrsystemrelevanz als Leitdifferenz des Qualitätsjournalismus. In K. Imhof, R. Blum & H. Bonfadelli (Hrsg.), Krise der Leuchttürme öffentlicher Kommunikation – Vergangenheit und Zukunft der Qualitätsmedien (S. 31–47). Wiesbaden: Springer.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2018

Authors and Affiliations

  • Henrik Müller
    • 1
    Email author
  • Gerret von Nordheim
    • 1
  • Karin Boczek
    • 1
  • Lars Koppers
    • 2
  • Jörg Rahnenführer
    • 2
  1. 1.Institut für JournalistikTechnische Universität DortmundDortmundDeutschland
  2. 2.Fakultät Statistik der Technischen Universität DortmundDortmundDeutschland

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