2004
Volume 1, Issue 1
  • ISSN: 2665-9085
  • E-ISSN: 2665-9085

Abstract

Abstract

This article introduces the interface for communication research (iCoRe) to access, explore, and analyze the Global Database of Events, Language, and Tone (GDELT; Leetaru & Schrodt, 2013). GDELT provides a vast, open source, and continuously updated repository of online news and event metadata collected from tens of thousands of news outlets around the world. Despite GDELT’s promise for advancing communication science, its massive scale and complex data structures have hindered efforts of communication scholars aiming to access and analyze GDELT. We thus developed iCoRe, an easy-to-use web interface that (a) provides fast access to the data available in GDELT, (b) shapes and processes GDELT for theory-driven applications within communication research, and (c) enables replicability through transparent query and analysis protocols. After providing an overview of how GDELT’s data pertain to addressing communication research questions, we provide a tutorial of utilizing iCoRe across three theory-driven case studies. We conclude this article with a discussion and outlook of iCoRe’s future potential for advancing communication research.

Loading

Article metrics loading...

/content/journals/10.5117/CCR2019.1.002.HOPP
2019-10-01
2021-09-20
Loading full text...

Full text loading...

/deliver/fulltext/26659085/1/1/02_CCR2019.1_HOPP.html?itemId=/content/journals/10.5117/CCR2019.1.002.HOPP&mimeType=html&fmt=ahah

References

  1. Arendt, F., & Karadas, N.(2017). Content analysis of mediated associations: An automated text-analytic approach. Communication Methods and Measures, 11(2), 105–120. doi:10.1080/19312458.2016.1276894
    [Google Scholar]
  2. Baccianella, S., Esuli, A., & Sebastiani, F. (2010, May). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Lrec, 10, 2200–2204. Retrieved from: https://esuli.it/publications/LREC2010.pdf
    [Google Scholar]
  3. Bowman, N., Lewis, R. J., & Tamborini, R.(2014). The morality of May 2, 2011: A content analysis of US headlines regarding the death of Osama bin Laden. Mass Communication and Society, 17(5), 639–664. doi:10.1080/15205436.2013.822518
    [Google Scholar]
  4. Barel, A., Hopp, F. R., & Weber, R.(2018). The moral framing of human rights reports: An exploratory data analysis of the human rights global knowledge graph. Poster presented at the 2018 Summer Undergraduate Research Experience Project Showcase, University of California, Santa Barbara, USA.
  5. Boxell, L., Gentzkow, M., & Shapiro, J. M.(2017). Greater Internet use is not associated with faster growth in political polarization among US demographic groups. Proceedings of the National Academy of Sciences, 114(40), 10612–10617. doi:10.1073/pnas.1706588114
    [Google Scholar]
  6. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C.(1994). Time series analysis: Forecasting and control (3rd edition). Englewood Cliffs, N.J.: Prentice Hall.
  7. Boyd, D., & Crawford, K.(2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
    [Google Scholar]
  8. Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J.(2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. doi:10.1073/pnas.1618923114
    [Google Scholar]
  9. Eilders, C.(2006). News factors and news decisions. Theoretical and methodological advances in Germany. Communications, 31(1), 5–24. doi:10.1515/COMMUN.2006.002
    [Google Scholar]
  10. Entman, R. M.(1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58. doi:10.1111/j.1460‑2466.1993.tb01304.x
    [Google Scholar]
  11. Feinberg, M., & Willer, R.(2013). The moral roots of environmental attitudes. Psychological Science, 24(1), 56–62. doi:10.1177/0956797612449177
    [Google Scholar]
  12. Feinberg, M., & Willer, R.(2015). From gulf to bridge: When do moral arguments facilitate political influence?Personality and Social Psychology Bulletin, 41(12), 1665–1681. doi:10.1177/0146167215607842
    [Google Scholar]
  13. Fisher, J., Cornell, D., Hopp, F. R., Weber, R. (2018, May). But how are they talked about?”: A novel measure of entity framing in online news. Paper presented at the annual meeting of the International Communication Association (ICA), Prague, Czech Republic, Prague, CZ.
  14. Fox, A., Eichelberger, C., Hughes, J., & Lyon, S.(2013). Spatio-temporal indexing in non-relational distributed databases. IEEE International Conference on Big Data (pp. 291–299). doi:10.1109/BigData.2013.6691586
    [Google Scholar]
  15. Früh, W., & Schönbach, K.(1982). The dynamic-transactional approach. A new paradigm of media effects. Publizistik, 27, 74–88.
    [Google Scholar]
  16. Fulgoni, D., Carpenter, J., Ungar, L. H., & Preotiuc-Pietro, D.(2016). An empirical exploration of moral foundations theory in partisan news sources. LREC. Retrieved from: www.lrec-conf.org/proceedings/lrec2016/pdf/1076_Paper.pdf
    [Google Scholar]
  17. Galtung, J., & Ruge, M. H.(1965). The structure of foreign news: The presentation of the Congo, Cuba and Cyprus crises in four Norwegian newspapers. Journal of Peace Research, 2(1), 64–90. doi:10.1177/002234336500200104
    [Google Scholar]
  18. Gerner, D. J., Schrodt, P. A., Yilmaz, O., & Abu-Jabr, R.(2002). Conflict and mediation event observations (cameo): A new event data framework for the analysis of foreign policy interactions. International Studies Association, New Orleans.
    [Google Scholar]
  19. Goldstein, J. S.(1992). A conflict-cooperation scale for WEIS events data. Journal of Conflict Resolution, 36(2), 369–385.
    [Google Scholar]
  20. Grimmer, J., & Stewart, B. M.(2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297. doi:10.1093/pan/mps028
    [Google Scholar]
  21. Guo, L., & Vargo, C. J.(2017). Global intermedia agenda setting: A big data analysis of international news flow. Journal of Communication, 67(4), 499–520. doi:10.1111/jcom.12311
    [Google Scholar]
  22. Guo, L., & Vargo, C. J.(2018). “Fake news” and emerging online media ecosystem: An integrated intermedia agenda-setting analysis of the 2016 US presidential election. Communication Research, 1–23. doi:10.1177/0093650218777177
    [Google Scholar]
  23. Graham, J., Haidt, J., & Nosek, B. A.(2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96(5), 1029–1046. doi:10.1037/a0015141
    [Google Scholar]
  24. Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H.(2011). Mapping the moral domain. Journal of Personality and Social Psychology, 101(2), 366–385. doi:10.1037/a0021847
    [Google Scholar]
  25. Greenwald, A. G.(2012). There is nothing so theoretical as a good method. Perspectives on Psychological Science, 7(2), 99–108. doi:10.1177/1745691611434210
    [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(3), 267–297. doi:10.1093/pan/mps028
    [Google Scholar]
  27. Hester, J. B., & Dougall, E.(2007). The efficiency of constructed week sampling for content analysis of online news. Journalism & Mass Communication Quarterly, 84(4), 811–824. doi:10.1177/107769900708400410
    [Google Scholar]
  28. Hilbert, M., & López, P.(2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65. doi:10.1126/science.1200970
    [Google Scholar]
  29. Hogenraad, R.(2003). The words that predict the outbreak of wars. Empirical studies of the Arts, 21(1), 5–20. doi:10.2190/HJWQ‑QRBX‑0C2E‑VJYA
    [Google Scholar]
  30. Hopkins, D. J., & King, G.(2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229–247. doi:10.1111/j.1540‑5907.2009.00428.x
    [Google Scholar]
  31. Hopp, F. R., Fisher, J., & Weber, R. (2019, May). The dynamic relationship between news frames and real-world events: A hidden markov model approach. Paper presented at the annual meeting of the International Communication Association (ICA), Washington D.C., USA.
  32. Hopp, F. R., Cornell, D., Fisher, J., Huskey, R., & Weber, R. (2018, November). The moral foundations dictionary for news (MFD-N): A crowd-sourced moral foundations dictionary for the automated analysis of news corpora. Paper presented at the annual Convention of the National Communication Association, Salt Lake City, UT, USA.
  33. Huberman, B. A.(2012). Sociology of science: Big data deserve a bigger audience. Nature, 482(7385), 308. doi:10.1038/482308d
    [Google Scholar]
  34. Hutto, C.J. & Gilbert, E.E.(2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI.
  35. LaFree, G., & Dugan, L.(2007). Introducing the global terrorism database. Terrorism and Political Violence, 19(2), 181–204. doi:10.1080/09546550701246817
    [Google Scholar]
  36. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., ... & Jebara, T.(2009). Computational social science. Science, 323(5915), 721–723. doi:10.1126/science.1167742
    [Google Scholar]
  37. Leetaru, K.(2011). Culturomics 2.0: Forecasting large-scale human behavior using global news media tone in time and space. First Monday, 16(9). Retrieved from https://firstmonday.org/ojs/index.php/fm/article/view/3663/3040
    [Google Scholar]
  38. Leetaru, K.(2013). The GDELT global knowledge graph (GKG). Available at gdeltproject.org/
    [Google Scholar]
  39. Leetaru, K., & Schrodt, P. A, (2013). GDELT: Global data on events, location and tone, 1979–2012. Paper presented at the International Studies Association Meeting, San Francisco, CA, USA. Retrieved from data.gdeltproject.org/documentation/ISA.2013.GDELT.pdf
    [Google Scholar]
  40. Lin, J.(2015). On building better mousetraps and understanding the human condition: Reflections on big data in the social sciences. The ANNALS of the American Academy of Political and Social Science, 659(1), 33–47. doi:10.1177/0002716215569174
    [Google Scholar]
  41. Markowitz, E. M., & Shariff, A. F.(2012). Climate change and moral judgement. Nature Climate Change, 2(4), 243–247. doi:10.1038/nclimate1378
    [Google Scholar]
  42. Mastro, D., Enriquez, M., & Bowman, N. D.(2012). Morality subcultures and media production: How Hollywood minds the morals of its audience. In Tamborini, R. (Ed.) Media and the moral mind (pp. 99–116). Routledge.
    [Google Scholar]
  43. McCombs, M. E., & Shaw, D. L.(1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187. doi:10.1086/267990
    [Google Scholar]
  44. McCombs, M.(2005). A look at agenda-setting: Past, present and future. Journalism Studies, 6(4), 543–557. doi:10.1080/14616700500250438
    [Google Scholar]
  45. Milkman, K. L., & Berger, J.(2014). The science of sharing and the sharing of science. Proceedings of the National Academy of Sciences, 111(Supplement 4), 13642–13649. doi:10.1073/pnas.1317511111
    [Google Scholar]
  46. Mooijman, M., Hoover, J., Lin, Y., Ji, H., & Dehghani, M.(2018). Moralization in social networks and the emergence of violence during protests. Nature Human Behaviour, 2, 389–396. doi:10.1038/s41562‑018‑0353‑0
    [Google Scholar]
  47. Moniruzzaman, A. B. M., & Hossain, S. A.(2013). Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191. Retrieved from: https://arxiv.org/abs/1307.0191
    [Google Scholar]
  48. Montgomery, D.C., & Weatherby., G.(1980). Modeling and forecasting time series using transfer function and intervention methods, AIIE Transactions, 12(4), 289–307. doi:10.1080/05695558008974521
    [Google Scholar]
  49. Murray, D. R., & Schaller, M.(2010). Historical prevalence of infectious diseases within 230 geopolitical regions: A tool for investigating origins of culture. Journal of Cross-Cultural Psychology, 41(1), 99–108. doi:10.1177/0022022109349510
    [Google Scholar]
  50. Nisbet, M. C.(2009). Communicating climate change: Why frames matter for public engagement. Environment: Science and Policy for Sustainable Development, 51(2), 12–23. doi:10.3200/ENVT.51.2.12‑23
    [Google Scholar]
  51. Noelle-Neumann, E.(1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24(2), 43–51. doi:10.1111/j.1460‑2466.1974.tb00367.x
    [Google Scholar]
  52. Peng, R. D.(2011). Reproducible research in computational science. Science, 334(6060), 1226–1227. doi:10.1126/science.1213847
    [Google Scholar]
  53. Pennebaker, J. W., Francis, M. E., and Booth, R. J.(2001). Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates.
  54. Qiao, F., Li, P., Zhang, X., Ding, Z., Cheng, J., & Wang, H.(2017). Predicting social unrest events with hidden Markov models using GDELT. Discrete Dynamics in Nature and Society, 2017. doi:10.1155/2017/8180272
    [Google Scholar]
  55. Sagi, E., & Dehghani, M.(2014). Measuring moral rhetoric in text. Social Science Computer Review, 32(2), 132–144. doi:10.1177/0894439313506837
    [Google Scholar]
  56. Scheufele, D. A.(1999). Framing as a theory of media effects. Journal of Communication, 49(1), 103–122. doi:10.1111/j.1460‑2466.1999.tb02784.x
    [Google Scholar]
  57. Scholz, C., Baek, E. C., O’Donnell, M. B., Kim, H. S., Cappella, J. N., & Falk, E. B.(2017). A neural model of valuation and information virality. Proceedings of the National Academy of Sciences, 201615259. doi:10.1073/pnas.1615259114
    [Google Scholar]
  58. Smith, E. M., Smith, J., Legg, P., & Francis, S.(2017). Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models. In Chao, F., Schockaert, S., & Zhang, Q. (Eds.) Advances in Computational Intelligence Systems (pp. 191–202). Wiesbaden: Springer.
    [Google Scholar]
  59. Trilling, D., & Jonkman, J. G.(2018). Scaling up content analysis. Communication Methods and Measures, 12(2–3), 158–174. doi:10.1080/19312458.2018.1447655
    [Google Scholar]
  60. Trilling, D., Tolochko, P., & Burscher, B.(2017). From newsworthiness to shareworthiness: How to predict news sharing based on article characteristics. Journalism & Mass Communication Quarterly, 94(1), 38–60. doi:10.1177/1077699016654682
    [Google Scholar]
  61. Van Atteveldt, W., & Peng, T. Q.(2018). When communication meets computation: Opportunities, challenges, and pitfalls in computational communication science. Communication Methods and Measures, 12(2-3), 81–92. doi:10.1080/19312458.2018.1458084
    [Google Scholar]
  62. Vargo, C. J., & Guo, L.(2017). Networks, big data, and intermedia agenda setting: An analysis of traditional, partisan, and emerging online us news. Journalism & Mass Communication Quarterly, 94(4), 1031–1055. doi:10.1177/1077699016679976
    [Google Scholar]
  63. Vargo, C. J., Guo, L., & Amazeen, M. A.(2018). The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, 20(5), 2028–2049. doi:10.1177/1461444817712086
    [Google Scholar]
  64. Wallach, H.(2016). Computational social science: Towards a collaborative future. In R. M.Alvarez (Ed.), Computational social science: Discovery and prediction (p. 307). Cambridge, UK: Cambridge University Press.
    [Google Scholar]
  65. Wang, W., Kennedy, R., Lazer, D., & Ramakrishnan, N.(2016). Growing pains for global monitoring of societal events. Science, 353(6307), 1502–1503. doi:10.1126/science.aaf6758
    [Google Scholar]
  66. Weber, M. S.(2018). Methods and approaches to using web archives in computational communication research, Communication Methods and Measures, 12(2–3), 200–215, doi:10.1080/19312458.2018.1447657
    [Google Scholar]
  67. Weber, R., Mangus, J. M., Huskey, R., Hopp, F. R., Amir, O., Swanson, R., ... & Tamborini, R.(2018). Extracting latent moral information from text narratives: Relevance, challenges, and solutions. Communication Methods and Measures, 12(2–3), 119–139. doi:10.1080/19312458.2018.1447656
    [Google Scholar]
  68. Weber, R.(1993). The impact of outstanding events on television audience ratings of the Tagesschau against the background of the dynamic transactional model. Master thesis, University of the Arts, Berlin, Germany.
    [Google Scholar]
  69. Wessler, H., & Brüggemann, M.(2012). Transnational communication. An introduction. Wiesbaden: Springer-Verlag.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.5117/CCR2019.1.002.HOPP
Loading
/content/journals/10.5117/CCR2019.1.002.HOPP
Loading

Data & Media loading...

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