Toward a Stronger Theoretical Grounding of Computational Communication Science | Amsterdam University Press Journals Online
2004
  1. Home
  2. A-Z Publications
  3. Computational Communication Research
  4. Volume 3, Issue 2
  5. Article
Volume 3, Issue 2
  • E-ISSN: 2665-9085
file format pdf download PDF

Abstract

Abstract

Computational communication science (CCS) is embraced by many as a fruitful methodological approach to studying communication in the digital era. However, theoretical advances have not been considered equally important in CCS. Specifically, we observe an emphasis on mid-range and micro theories that misses a larger discussion on how macro-theoretical frameworks can serve CCS scholarship. With this article, we aim to stimulate such a discussion. Although macro frameworks might not point directly to specific questions and hypotheses, they shape our research through influencing which kinds of questions we ask, which kinds of hypotheses we formulate, and which methods we find adequate and useful. We showcase how three selected theoretical frameworks might advance CCS scholarship in this way: (1) complexity theory, (2) theories of the public sphere, and (3) mediatization theory. Using online protest as an example, we discuss how the focus (and the blind spots) of our research designs shifts with each framework.

© This is an open access article distributed under the terms of the CC BY 4.0 license
Loading

Article metrics loading...

/content/journals/10.5117/CCR2021.02.002.WALD
2021-10-01
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/26659085/3/2/CCR2021.2.002.WALD.html?itemId=/content/journals/10.5117/CCR2021.02.002.WALD&mimeType=html&fmt=ahah

References

  1. Allan, K. (2012). Contemporary social and sociological theory: Visualizing social worlds.London, UK: Sage.
     [Google Scholar]
  2. Alvarez, R. M. (2016). Computational social science: Discovery and prediction.New York, NY: Cambridge University Press.
     [Google Scholar]
  3. Andersen, J. (2018). Archiving, ordering, and searching: Search engines, algorithms, databases, and deep mediatization. Media, Culture & Society, 40(8), 1135–1150. https://doi.org/10.1177/0163443718754652
     [Google Scholar]
  4. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired. Retrieved from https://www.wired.com/2008/06/pb-theory/
     [Google Scholar]
  5. Asgharpourmasouleh, A., Fattahzadeh, M., Mayerhoffer, D., & Lorenz, J. (2020). On the fate of protests: Dynamics of social activation and topic selection online and in the streets. In E.Deutschmann, J.Lorenz, L. G.Nardin, D.Natalini, & A. F. X.Wilhelm (Eds.), Computational conflict research (pp. 141–164). Cham: Springer.
     [Google Scholar]
  6. Barabási, A.-L. (2016). Network science.Cambridge, UK: Cambridge University Press.
     [Google Scholar]
  7. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509
     [Google Scholar]
  8. Barberá, P., Wang, N., Bonneau, R., Jost, J. T., Nagler, J., Tucker, J., & González-Bailón, S. (2015). The critical periphery in the growth of social protests. PLoS One, 10(11), e0143611. https://doi.org/10.1371/journal.pone.0143611
     [Google Scholar]
  9. Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom.New Haven, CT: Yale University Press.
     [Google Scholar]
  10. Bennett, W. L., & Segerberg, A. (2013). The logic of connective action: Digital media and the personalization of contentious politics.New York, NY: Cambridge University Press.
     [Google Scholar]
  11. Bennett, W. L., Segerberg, A., & Yang, Y. (2018). The strength of peripheral networks: Negotiating attention and meaning in complex media ecologies. Journal of Communication, 68(4), 659–684. https://doi.org/10.1093/joc/jqy032
     [Google Scholar]
  12. Blumler, J. G. (2015). Core theories of political communication: Foundational and freshly minted. Communication Theory, 25(4), 426–438. https://doi.org/10.1111/comt.12077
     [Google Scholar]
  13. Bolsover, G., & Howard, P. (2017). Computational propaganda and political big data: Moving toward a more critical research agenda. Big data, 5(4), 273–276. https://doi.org/10.1089/big.2017.29024.cpr
     [Google Scholar]
  14. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295. doi:10.1038/nature11421
     [Google Scholar]
  15. bboyd, D. (2010). Social network sites as networked publics: Affordances, dynamics, and implications. In Z.Papacharissi (Ed.), Networked self: Identity, community, and culture on social network sites (pp. 39–58). London, UK: Routledge.
     [Google Scholar]
  16. Brantner, C., & Rodriguez-Amat, J. R. (2016). New “danger zone” in Europe: Representations of place in social media–supported protests. International Journal of Communication, 10, 299–320. Retrieved from https://ijoc.org/index.php/ijoc/article/view/3788
     [Google Scholar]
  17. Broido, A. D., & Clauset, A. (2019). Scale-free networks are rare. Nature Communications, 10(1), 1017. https://doi.org/10.1038/s41467-019-08746-5
     [Google Scholar]
  18. Bruns, A., & Highfield, T. (2016). Is Habermas on Twitter? Social media and the public sphere. In A.Bruns, G.Enli, E.Skogerbø, A. O.Larsson, & C.Christensen (Eds.), The Routledge companion to social media and politics (pp. 56–73). New York, NY: Routledge.
     [Google Scholar]
  19. Casas, A., & Williams, N. W. (2018). Images that matter: Online protests and the mobilizing role of pictures. Political Research Quarterly, 72(2), 360–375. https://doi.org/10.1177/1065912918786805
     [Google Scholar]
  20. Centola, D. M. (2013). Homophily, networks, and critical mass: Solving the start-up problem in large group collective action. Rationality and Society, 25(1), 3–40. https://doi.org/10.1177/1043463112473734
     [Google Scholar]
  21. Choi, S. (2020). When digital trace data meet traditional communication theory: Theoretical/methodological directions. Social Science Computer Review, 38(1), 91–107. https://doi.org/10.1177/0894439318788618
     [Google Scholar]
  22. Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., ... Helbing, D. (2012). Manifesto of computational social science. European Physical Journal Special Topics, 214, 325–346. https://doi.org/10.1140/epjst/e2012-01697-8
     [Google Scholar]
  23. Couldry, N., & Hepp, A. (2013). Conceptualizing mediatization: Contexts, traditions, arguments. Communication Theory, 23(3), 191–202. https://doi.org/10.1111/comt.12019
     [Google Scholar]
  24. Couldry, N., & Hepp, A. (2016). The mediated construction of reality. Cambridge, UK: Polity Press.
     [Google Scholar]
  25. Crawford, K. (2013). The hidden biases in big data. Harvard Business Review Blog. Retrieved from https://www.hbrianholland.com/s/05-The-Hidden-Biases-in-Big-Data-Crawford.pdf
     [Google Scholar]
  26. Dahlberg, L. (2018). Visibility and the public sphere: A normative conceptualisation. Javnost, 25(1–2), 35–42. https://doi.org/10.1080/13183222.2018.1418818
     [Google Scholar]
  27. Daubs, M. S. (2017). The myth of an egalitarian Internet: Occupy Wall Street and the mediatization of social movements. International Journal of Digital Television, 8(3), 367–382. https://doi.org/10.1386/jdtv.8.3.367_1
     [Google Scholar]
  28. Earl, J. (2010). The dynamics of protest-related diffusion on the web. Information, Communication & Society, 13(2), 209–225. https://doi.org/10.1080/13691180902934170
     [Google Scholar]
  29. Epstein, J. M. (2002). Modeling civil violence: An agent-based computational approach. Proceedings of the National Academy of Sciences, 99(suppl. 3), 7243–7250. https://doi.org/10.1073/pnas.092080199
     [Google Scholar]
  30. Ferree, M., Gamson, W., Gerhards, J., & Rucht, D. (2002). Four models of the public sphere in modern democracies. Theory and Society, 31(3), 289–324. https://doi.org/10.1023/A:1016284431021
     [Google Scholar]
  31. Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: Towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20(4), 2. https://doi.org/10.18564/jasss.3521
     [Google Scholar]
  32. Foucault Welles, B., & González-Bailón, S. (Eds.). (2020). The Oxford handbook of networked communication. New York, NY: Oxford University Press.
     [Google Scholar]
  33. Freelon, D. G. (2010). Analyzing online political discussion using three models of democratic communication. New Media & Society, 12(7), 1172–1190. https://doi.org/10.1177/1461444809357927.
     [Google Scholar]
  34. Geise, S. & Waldherr, A. (in press). Computational communication science: Lessons from working group sessions with experts of an emerging research field. In U.Engel, A.Quan-Haase, A., S. X.Liu, & L.Lyberg (Eds.), Handbook of computational social science. Volume 1: Theory, case studies and ethics.London, UK: Routledge.
     [Google Scholar]
  35. González-Bailón, S. (2017). Decoding the social world: Data science and the unintended consequences of communication.Cambridge, MA: MIT Press.
     [Google Scholar]
  36. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 6(83), 1420–1443. https://doi.org/10.1086/226707
     [Google Scholar]
  37. Habermas, J. (2006). Political communication in media society: Does democracy still enjoy an epistemic dimension? The impact of normative theory on empirical research. Communication Theory, 16(4), 411–426. https://doi.org/10.1111/j.1468-2885.2006.00280.x
     [Google Scholar]
  38. Hamill, L., & Gilbert, N. (2009). Social circles: A simple structure for agent-based social network models. Journal of Artificial Societies and Social Simulation, 12(2), 3. Retrieved from http://jasss.soc.surrey.ac.uk/12/2/3.html
     [Google Scholar]
  39. Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites. The ANNALS of the American Academy of Political and Social Science, 659(1), 63–76. https://doi.org/10.1177/0002716215570866
     [Google Scholar]
  40. Hepp, A. (2009). Differentiation: Mediatization and cultural change. In K.Lundby (Ed.), Mediatization: Concept, changes, consequences (pp. 135–154). New York, NY: Lang.
     [Google Scholar]
  41. Hepp, A., Breiter, A., & Friemel, T. N. (2018). Digital traces in context: An introduction. International Journal of Communication, 12, 439–449. Retrieved from https://ijoc.org/index.php/ijoc/article/view/8650
     [Google Scholar]
  42. Hepp, A. & Hasebrink, U. (2018). Researching transforming communications in times of deep mediatization: A figurational approach. In A.Hepp, A.Breiter, & U.Hasebrink (Eds.), Communicative figurations (pp. 15–49). Cham: Palgrave Macmillan.
     [Google Scholar]
  43. Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., … & Zhu, J. J. H. (2019). Computational communication science: A methodological catalyzer for a maturing discipline. International Journal of Communication, 13, 3912–3934. Retrieved from https://www.ijoc.org/index.php/ijoc/article/view/10675
     [Google Scholar]
  44. Hjarvard, S. (2013). The mediatization of culture and society.New York, NY: Routledge.
     [Google Scholar]
  45. Hu, H.-H., Cui, W.-T., Lin, J., & Qian, Y.-J. (2014). ICTs, social connectivity, and collective action: A cultural-political perspective. Journal of Artificial Societies and Social Simulation, 17(2), 7. https://doi.org/10.18564/jasss.2486
     [Google Scholar]
  46. Hussain, M. M., & Howard, P. N. (2013). What best explains successful protest cascades? ICTs and the fuzzy causes of the Arab Spring. International Studies Review, 15(1), 48–66. https://doi.org/10.1111/misr.12020
     [Google Scholar]
  47. Jackson, S. J., & Foucault Welles, B. (2015). Hijacking #myNYPD: Social media dissent and networked counterpublics. Journal of Communication, 65(6), 932–952. https://doi.org/10.1111/jcom.12185
     [Google Scholar]
  48. Jacobi, C., Van Atteveldt, W., & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism, 4(1), 89–106. doi:10.1080/21670811.2015.1093271
     [Google Scholar]
  49. Kaiser, J., & Puschmann, C. (2017). Alliance of antagonism: Counterpublics and polarization in online climate change communication. Communication and the Public, 2(4), 371–387. https://doi.org/10.1177/2057047317732350
     [Google Scholar]
  50. Kapidzic, S., Neuberger, C., Stieglitz, S., & Mirbabaie, M. (2018). Interaction and influence on Twitter: Comparing the discourse relationships between user types on five topics. Digital Journalism, 7(2), 251–272. https://doi.org/10.1080/21670811.2018.1522962
     [Google Scholar]
  51. Keller, T. R., & Klinger, U. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171–189. https://doi.org/10.1080/10584609.2018.1526238
     [Google Scholar]
  52. Krotz, F. (2007). The meta-process of mediatization as a conceptual frame. Global Media and Communication, 3(3), 256–260. https://doi.org/10.1177/17427665070030030103
     [Google Scholar]
  53. Kubitschko, S. (2018). Chaos Computer Club: The communicative construction of media technologies and infrastructures as a political category. In A.Hepp, A.Breiter, & U.Hasebrink (Eds.), Communicative figurations (pp. 81–100). Cham: Palgrave Macmillan.
     [Google Scholar]
  54. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., ... Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
     [Google Scholar]
  55. Lipsky, M. (1968). Protest as a political resource. American Political Science Review, 62(4), 1144–1158. https://doi.org/10.2307/1953909
     [Google Scholar]
  56. Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models for social networks: Theory, methods, and applications.New York, NY: Cambridge University Press.
     [Google Scholar]
  57. Mahrt, M. (2018). Big data. In P. M.Napoli (Ed.), Mediated communication (pp. 627–642). Berlin: Mouton De Gruyter.
     [Google Scholar]
  58. Mattoni, A., & Treré, E. (2014). Media practices, mediation processes, and mediatization in the study of social movements. Communication Theory, 24(3), 252–271. https://doi.org/10.1111/comt.12038
     [Google Scholar]
  59. Mayer-Schönberger, V., & Cukier, K. (2013). Big data. A revolution that will transform how we live, work, and think. London: Murray.
     [Google Scholar]
  60. Mazzoleni, G., & Schulz, W. (1999). “Mediatization” of politics: A challenge for democracy?Political communication, 16(3), 247–261. https://doi.org/10.1080/105846099198613
     [Google Scholar]
  61. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press.
     [Google Scholar]
  62. Nuernbergk, C. (2014). Follow-up communication in the blogosphere. Digital Journalism, 2(3), 434–445. https://doi.org/10.1080/21670811.2014.895520
     [Google Scholar]
  63. Ophir, Y., Walter, D., & Marchant, E. R. (2020). A collaborative way of knowing: Bridging computational communication research and grounded theory ethnography. Journal of Communication, 70(3), 447–472. https://doi.org/10.1093/joc/jqaa013
     [Google Scholar]
  64. Papacharissi, Z. (2009). The virtual sphere 2.0: The internet, the public sphere, and beyond. In A.Chadwick & P. N.Howard (Eds.), Routledge handbook of internet politics (pp. 230–245). London, UK: Routledge.
     [Google Scholar]
  65. Piedrahita, P., Borge-Holthoefer, J., Moreno, Y., & González-Bailón, S. (2018). The contagion effects of repeated activation in social networks. Social Networks, 54, 326–335. https://doi.org/10.1016/j.socnet.2017.11.001
     [Google Scholar]
  66. Rittel, H. W. J., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155–169. https://doi.org/10.1007/BF01405730
     [Google Scholar]
  67. Resnyansky, L. (2019). Conceptual frameworks for social and cultural big data analytics: Answering the epistemological challenge. Big Data & Society, 6(1). https://doi.org/10.1177/2053951718823815
     [Google Scholar]
  68. Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. https://doi.org/10.1126/science.346.6213.1063
     [Google Scholar]
  69. Sawyer, K. R. (2005). Social emergence: Societies as complex systems. Cambridge, UK: Cambridge University Press.
     [Google Scholar]
  70. Schäfer, M. S. (2015). Digital public sphere. In G.Mazzoleni (Ed.), The international encyclopedia of political communication (pp. 322–328). London, UK: Wiley Blackwell.
     [Google Scholar]
  71. Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. https://doi.org/10.1177/0002716215572084
     [Google Scholar]
  72. Sherry, J. L. (2015). The complexity paradigm for studying human communication: A summary and integration of two fields. Review of Communication, 3(1), 22–54. https://doi.org/10.12840/issn.2255-4165.2015.03.01.007
     [Google Scholar]
  73. Theocharis, Y. (2013). The wealth of (occupation) networks? Communication patterns and information distribution in a Twitter protest network. Journal of Information Technology & Politics, 10(1), 35–56. https://doi.org/10.1080/19331681.2012.701106
     [Google Scholar]
  74. 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. https://doi.org/10.1080/19312458.2018.1458084
     [Google Scholar]
  75. 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. https://doi.org/10.1177/1461444817712086
     [Google Scholar]
  76. WaldherrA., Geise, S., & Katzenbach, C. (2019). Because technology matters: Theorizing interdependencies in computational communication science with actor-network theory. International Journal of Communication, 13, 3955–3975. Retrieved from https://www.ijoc.org/index.php/ijoc/article/view/10580
     [Google Scholar]
  77. Waldherr, A., & Wettstein, M. (2019). Bridging the gaps: Using agent-based modeling to reconcile data and theory in computational communication science. International Journal of Communication, 13, 3976–3999. Retrieved from https://www.ijoc.org/index.php/ijoc/article/view/10588
     [Google Scholar]
  78. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. https://doi.org/10.1038/30918
     [Google Scholar]
  79. Weber, E. P., & Khademian, A. M. (2008). Wicked problems, knowledge challenges, and collaborative capacity builders in network settings. Public Administration Review, 68, 334–349. https://doi.org/10.1111/j.1540-6210.2007.00866.x
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.5117/CCR2021.02.002.WALD
Loading
Toward a Stronger Theoretical Grounding of Computational Communication Science
CCR 3, 1 (2021); https://doi.org/10.5117/CCR2021.02.002.WALD
/content/journals/10.5117/CCR2021.02.002.WALD
/content/journals/10.5117/CCR2021.02.002.WALD
Loading

Data & Media loading...

Keyword(s): Communication Theory; Complexity Theory; Computational Communication Science; Computational Methods; Mediatization; Public Sphere

Most Cited Most Cited RSS feed

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