2004
Volume 2, Issue 1
  • ISSN: 2665-9085
  • E-ISSN: 2665-9085

Abstract

Abstract

When addressing public concerns such as the existence of politically like-minded communication spaces in social media, analyses of complex political discourses are met with increasing methodological challenges to process communication data properly. To address the extent of political like-mindedness in online communication, we argue that it is necessary to focus not only on ideological homogeneity in online environments, but also on the extent to which specific political questions are discussed in a uniform manner. This study proposes an innovative combination of computational methods, including natural language processing and social network analysis, that serves as a model for future research examining the evolution of opinion climates in online networks. Data were gathered on YouTube, enabling the assessment of users’ expressed opinions on three political issues (i.e., adoption rights for same-sex couples, headscarf rights, and climate change). Challenging widely held assumptions on discursive homogeneity online, the results provide evidence for a moderate level of connections between dissimilar YouTube comments but few connections between agreeing comments. The findings are discussed in light of current computational communication research and the vigorous debate on the prevalence of like-mindedness in online networks.

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2020-02-01
2021-12-06
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References

  1. Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B.(2018). Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science, 27, 386–393. https://doi.org/10.1016/j.jocs.2017.11.006
    [Google Scholar]
  2. Bakshy, E., Messing, S., & Adamic, L. A.(2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
    [Google Scholar]
  3. Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R.(2015). Tweeting from left to right: Is online political communication more than an echo chamber?Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620
    [Google Scholar]
  4. Beam, M. A., Hutchens, M. J., & Hmielowski, J. D.(2018). Facebook news and (de)polarization: Reinforcing spirals in the 2016 US election. Information, Communication & Society, 21(7), 940–958. https://doi.org/10.1080/1369118X.2018.1444783
    [Google Scholar]
  5. Bello-Orgaz, G., Hernandez-Castro, J., & Camacho, D.(2017). Detecting discussion communities on vaccination in twitter. Future Generation Computer Systems, 66, 125–136. https://doi.org/10.1016/j.future.2016.06.032
    [Google Scholar]
  6. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E.(2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/p10008
    [Google Scholar]
  7. Bond, R., & Messing, S.(2015). Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on Facebook. American Political Science Review, 109(1), 62–78. https://doi.org/10.1017/S0003055414000525
    [Google Scholar]
  8. Boutyline, A., & Willer, R.(2017). The social structure of political echo chambers: Variation in ideological homophily in online networks. Political Psychology, 38(3), 551–569. https://doi.org/10.1111/pops.12337
    [Google Scholar]
  9. Bright, J.(2018). Explaining the emergence of political fragmentation on social media: The role of ideology and extremism. Journal of Computer-Mediated Communication, 23(1), 17–33. https://doi.org/10.1093/jcmc/zmx002
    [Google Scholar]
  10. Chan, C., & Fu, K.(2017). The relationship between cyberbalkanization and opinion polarization: Time-series analysis on Facebook pages and opinion polls during the Hong Kong Occupy Movement and the associated debate on political reform. Journal of Computer-Mediated Communication, 22(5), 266–283. https://doi.org/10.1111/jcc4.12192
    [Google Scholar]
  11. Chang, C.-C., & Lin, C.-J.(2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27:1–27:27. https://doi.org/10.1145/1961189.1961199
    [Google Scholar]
  12. Clauset, A., Newman, M. E. J., & Moore, C.(2004). Finding community structure in very large networks. Phys. Rev. E, 70(6), 066111. https://doi.org/10.1103/PhysRevE.70.066111
    [Google Scholar]
  13. Colleoni, E., Rozza, A., & Arvidsson, A.(2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317–332. https://doi.org/10.1111/jcom.12084
    [Google Scholar]
  14. Del Valle, M. E., & Bravo, R. B.(2018). Echo Chambers in parliamentary Twitter networks: The Catalan case. International Journal of Communication, 12, 21.
    [Google Scholar]
  15. Eveland, W. P., & Kleinman, S. B.(2013). Comparing general and political discussion networks within voluntary organizations using social network analysis. Political Behavior, 35(1), 65–87. https://doi.org/10.1007/s11109-011-9187-4
    [Google Scholar]
  16. Flaxman, S., Goel, S., & Rao, J. M.(2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320. https://doi.org/10.1093/poq/nfw006
    [Google Scholar]
  17. Geschke, D., Lorenz, J., & Holtz, P.(2019). The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology, 58(1), 129–149. https://doi.org/10.1111/bjso.12286
    [Google Scholar]
  18. Graham, T.(2015). Everyday political talk in the internet-based public sphere. In S.Coleman & D.Freelon (Eds), Handbook of digital politics (pp. 247–263). Cheltenham, UK: Edward Elgar Publishing.
    [Google Scholar]
  19. Guo, L., Rohde, J. A., & Wu, H. D.(2018). Who is responsible for Twitter’s echo chamber problem? Evidence from 2016 US election networks. Information, Communication & Society. https://doi.org/10.1080/1369118X.2018.1499793
    [Google Scholar]
  20. Häussler, T.(2018). Heating up the debate? Measuring fragmentation and polarisation in a German climate change hyperlink network. Social Networks, 54, 303–313. https://doi.org/10.1016/j.socnet.2017.10.002
    [Google Scholar]
  21. Hayes, A. F., & Krippendorff, K.(2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77–89. https://doi.org/10.1080/19312450709336664
    [Google Scholar]
  22. Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M., & West, K.(2016). Valence-based homophily on Twitter: Network analysis of emotions and political talk in the 2012 presidential election. New Media & Society, 18(7), 1382–1400. https://doi.org/10.1177/1461444814555096
    [Google Scholar]
  23. Janssen, D., & Kies, R.(2005). Online forums and deliberative democracy. Acta Politica, 40(3), 317–335. https://doi.org/10.1057/palgrave.ap.5500115
    [Google Scholar]
  24. Jasny, L., Waggle, J., & Fisher, D. R.(2015). An empirical examination of echo chambers in US climate policy networks. Nature Climate Change, 5(8), 782. https://doi.org/10.1038/nclimate2666
    [Google Scholar]
  25. Jordan, M. I., & Mitchell, T. M.(2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
    [Google Scholar]
  26. Kim, M.(2018). How does Facebook news use lead to actions in South Korea? The role of Facebook discussion network heterogeneity, political interest, and conflict avoidance in predicting political participation. Telematics and Informatics, 35(5), 1373–1381. https://doi.org/10.1016/j.tele.2018.03.007
    [Google Scholar]
  27. Knobloch-Westerwick, S.(2014). Choice and preference in media use: Advances in selective exposure theory and research. Routledge.
  28. Krackhardt, D., & Stern, R. N.(1988). Informal Networks and Organizational Crises: An Experimental Simulation. Social Psychology Quarterly, 51(2), 123–140. https://doi.org/10.2307/2786835
    [Google Scholar]
  29. Lee, J. K., Choi, J., Kim, C., & Kim, Y.(2014). Social media, network heterogeneity, and opinion polarization. Journal of Communication, 64(4), 702–722. https://doi.org/10.1111/jcom.12077
    [Google Scholar]
  30. Levendosky, A. A., Bogat, G. A., Theran, S. A., Trotter, J. S., Eye, A. von, & Davidson, W. S.(2004). The social networks of women experiencing domestic violence. American Journal of Community Psychology, 34(1–2), 95–109. https://doi.org/10.1023/B:AJCP.0000040149.58847.10
    [Google Scholar]
  31. Lu, Y., & Lee, J. K.(2018). Stumbling upon the other side: Incidental learning of counter-attitudinal political information on Facebook. New Media & Society, 1461444818793421. https://doi.org/10.1177/1461444818793421
    [Google Scholar]
  32. McPherson, M., Smith-Lovin, L., & Cook, J. M.(2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
    [Google Scholar]
  33. Mercea, D., & Yilmaz, K. E.(2018). Movement social learning on Twitter: The case of the People’s Assembly. The Sociological Review, 66(1), 20–40. https://doi.org/10.1177/0038026117710536
    [Google Scholar]
  34. Min, S.-J.(2007). Online vs. Face-to-face deliberation: Effects on civic engagement. Journal of Computer-Mediated Communication, 12(4), 1369–1387. https://doi.org/10.1111/j.1083-6101.2007.00377.x
    [Google Scholar]
  35. Neubaum, G., & Krämer, N. C.(2017). Monitoring the opinion of the crowd: Psychological mechanisms underlying public opinion perceptions on social media. Media Psychology, 20(3), 502–531.
    [Google Scholar]
  36. Newman, M. E. J.(2004). Fast algorithm for detecting community structure in networks. Phys. Rev. E, 69(6), 066133. https://doi.org/10.1103/PhysRevE.69.066133
    [Google Scholar]
  37. Noelle-Neumann, E., & Petersen, T.(2004). The spiral of silence and the social nature of man. In Handbook of political communication research (pp. 357–374). Routledge.
    [Google Scholar]
  38. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E.(2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
    [Google Scholar]
  39. Pons, P., & Latapy, M.(2006). Computing communities in large networks using random walks. J. Graph Algorithms Appl., 10(2), 191–218. https://doi.org/10.1007/11569596_31
    [Google Scholar]
  40. Prior, M.(2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. Cambridge, NY: Cambridge University Press.
  41. Rojas, H.(2010). “Corrective” actions in the public sphere: How perceptions of media and media effects shape political behaviors. International Journal of Public Opinion Research, 22(3), 343–363. https://doi.org/10.1093/ijpor/edq018
    [Google Scholar]
  42. Schneider, S. M.(1996). Creating a democratic public sphere through political discussion: A case study of abortion conversation on the Internet. Social Science Computer Review, 14(4), 373–393. https://doi.org/10.1177/089443939601400401
    [Google Scholar]
  43. Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C.(2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168.
    [Google Scholar]
  44. Sunstein, C. R.(2017). # Republic: Divided democracy in the age of social media. Princeton University Press.
  45. Vaccari, C., Valeriani, A., Barberá, P., Jost, J. T., Nagler, J., & Tucker, J. A.(2016). Of echo chambers and contrarian clubs: Exposure to political disagreement among German and Italian users of Twitter. Social Media + Society, 2(3), 1–24. https://doi.org/10.1177/2056305116664221
    [Google Scholar]
  46. Williams, H. T., McMurray, J. R., Kurz, T., & Lambert, F. H.(2015). Network analysis reveals open forums and echo chambers in social media discussions of climate change. Global Environmental Change, 32, 126–138. https://doi.org/10.1016/j.gloenvcha.2015.03.006
    [Google Scholar]
  47. Yan, B., Yang, Z., Ren, Y., Tan, X., & Liu, E.(2017). Microblog sentiment classification Using parallel SVM in Apache Spark. 2017 IEEE International Congress on Big Data (BigData Congress), 282–288. https://doi.org/10.1109/BigDataCongress.2017.43
    [Google Scholar]
  48. YouGov, & BRAVO. (2017). Politische Jugendstudie [Political youth study]. Retrieved from https://campaign.yougov.com/DE_2017_07_Political_Bravo_Jugendstudie_DE_2017_06_Political_Die_Deutschen_und_die_Politik_Landing.html
  49. Zillmann, D., & Bryant, J. (Eds.). (1985). Selective exposure to communication. Hillsdale, N.J: L. Erlbaum Associates.
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