Characteristics of two polarized groups in online social networks’ controversial discourse | Journal of Computational Social Science Skip to main content

Advertisement

Springer Nature Link
Log in

Characteristics of two polarized groups in online social networks’ controversial discourse

  • Research Article
  • Published:
Journal of Computational Social Science Aims and scope Submit manuscript

Abstract

In today’s interconnected world, online social networks play a pivotal role in facilitating global communication. These platforms often host discussions on contentious topics such as climate change, vaccines, and war, leading to the formation of two distinct groups: deniers and believers. Understanding the characteristics of these groups is crucial for predicting information flow and managing the diffusion of information. Moreover, such understanding can enhance machine learning algorithms designed to automatically detect these groups, thereby contributing to the development of strategies to curb the spread of disinformation, including fake news and rumors. In this study, we employ social network analysis measures to extract the characteristics of these groups, conducting experiments on three large-scale datasets of over 22 million tweets. Our findings indicate that, based on network science measures, the denier (anti) group exhibits greater coherence than the believer (pro) group.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability statement

The data and codes used and/or analyzed during the current study available from the corresponding author on request.

Notes

  1. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2F5QCCUU.

  2. https://github.com/rapidsai/cugraph.

References

  1. Germani, F., & Biller-Andorno, N. (2021). The anti-vaccination infodemic on social media: A behavioral analysis. PLoS ONE, 16, e0247642.

    Article  Google Scholar 

  2. Jemielniak, D., & Krempovych, Y. (2021). An analysis of AstraZeneca COVID-19 vaccine misinformation and fear mongering on twitter. Public Health, 200, 4–6.

    Article  Google Scholar 

  3. Neff, T., Kaiser, J., Pasquetto, I., Jemielniak, D., Dimitrakopoulou, D., Grayson, S., Gyenes, N., Ricaurte, P., Ruiz-Soler, J., & Zhang, A. (2021). Vaccine hesitancy in online spaces: A scoping review of the research literature, 2000–2020. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-82

    Article  Google Scholar 

  4. Khaldarova, I., & Pantti, M. (2016). Fake news (pp. 891–901). Journalism Practice.

    Google Scholar 

  5. McIntyre, L. (2022). How to talk to a science denier: conversations with flat Earthers, climate deniers, and others who defy reason. MIT Press.

    Google Scholar 

  6. Marques, M. D., Kerr, J. R., Williams, M. N., Ling, M., & McLennan, J. (2021). Associations between conspiracism and the rejection of scientific innovations. Public understanding of science, 30, 854–867.

    Article  Google Scholar 

  7. McDevitt, M., Parks, P., Stalker, J., Lerner, K., Benn, J., & Hwang, T. (2018). Anti-intellectualism among US students in journalism and mass communication: A cultural perspective. Journalism, 19, 782–799.

    Article  Google Scholar 

  8. Okruszek, Ł, Piejka, A., Banasik-Jemielniak, N., & Jemielniak, D. (2022). Climate change, vaccines, GMO: The N400 effect as a marker of attitudes toward scientific issues. PLoS ONE, 17, e0273346.

    Article  Google Scholar 

  9. Walter, S., Brüggemann, M., & Engesser, S. (2018). Echo chambers of denial: explaining user comments on climate change. Environmental Communication, 12, 204–217.

    Article  Google Scholar 

  10. van Eck, C. W., Mulder, B. C., & van der Linden, S. (2021). Echo chamber effects in the climate change blogosphere. Environmental Communication, 15, 145–152.

    Article  Google Scholar 

  11. Erlich, A., & Garner, C. (2023). Is pro-Kremlin disinformation effective? Evidence from Ukraine. The International Journal of Press/Politics, 28, 5–28.

    Article  Google Scholar 

  12. Moy, Wesley R., Kacper Gradon, & Russian Covid- Effects. (2023). COVID -19 effects and Russian disinformation campaigns. https://www.hsaj.org/resources/uploads/2020/12/hsaj_Covid192020_COVID19EffectsRussianDisinformationCampaigns.pdf. Accessed June 26.

  13. Nowak, B. M., Miedziarek, C., Pełczyński, S., & Rzymski, P. (2021). Misinformation, fears and adherence to preventive measures during the early phase of COVID-19 pandemic: A cross-sectional study in Poland. International journal of environmental research and public health. https://doi.org/10.3390/ijerph182212266

    Article  Google Scholar 

  14. Jiang, B., Mansooreh K., Lu C., Black T., & Liu H. (2021). Mechanisms and attributes of echo chambers in social media.

  15. Alatawi, F., Cheng L., Tahir A., Karami M., Jiang B., Black T., & Liu H. (2021). A Survey on echo chambers on social media: description, detection and mitigation.

  16. Chay, S., & Sasaki, N. (2011). Using online tools to assess public responses to climate change mitigation policies in Japan. Future Internet, 3, 117–129.

    Article  Google Scholar 

  17. Auer, M. R., Zhang, Y., & Lee, P. (2014). The potential of microblogs for the study of public perceptions of climate change. Wiley interdisciplinary reviews: Climate change, 5, 291–296.

    Google Scholar 

  18. Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2014). Climate change on Twitter: Topics, communities and conversations about the 2013 IPCC Working Group 1 report. PLoS ONE, 9, e94785.

    Article  Google Scholar 

  19. Williams, H. T. P., McMurray, J. R., Kurz, T., & Hugo Lambert, F. (2015). Network analysis reveals open forums and echo chambers in social media discussions of climate change. Global environmental change: Human and policy dimensions, 32, 126–138.

    Article  Google Scholar 

  20. Holme, P., & Rocha J. C. (2021). Networks of climate change: Connecting causes and consequences. arXiv [physics.soc-ph]. arXiv.

  21. Baumann, F., Lorenz-Spreen, P., Sokolov, I. M., & Starnini, M. (2020). Modeling echo chambers and polarization dynamics in social networks. Physical Review Letters, 124, 048301.

    Article  Google Scholar 

  22. Tyagi, A., Babcock M., Carley K. M., & Sicker D. C. (2020). Polarizing tweets on climate change. In Social, cultural, and behavioral modeling, (pp. 107–117) Springer International Publishing.

  23. Neff, T., & Jemielniak, D. (2022). How do transnational public spheres emerge? Comparing news and social media networks during the Madrid climate talks. New Media & Society, 26, 2066–2091.

    Article  Google Scholar 

  24. Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate change sentiment on twitter: An unsolicited public opinion poll. PLoS ONE, 10, e0136092.

    Article  Google Scholar 

  25. Cinelli, M., De Francisci Morales G., Galeazzi A., Quattrociocchi W. & Starnini M. (2021). The echo chamber effect on social media. In Proceedings of the national academy of sciences of the United States of America, 118. https://doi.org/10.1073/pnas.2023301118

  26. Jasny, L., & Fisher, D. R. (2019). Echo chambers in climate science. Environmental Research Communications, 1, 101003.

    Article  Google Scholar 

  27. Samantray, A., & Pin, P. (2019). Credibility of climate change denial in social media. Palgrave Communications, 5, 1–8.

    Article  Google Scholar 

  28. Littman, J., & Wrubel, L. (2019). Climate change tweets Ids. Harvard Dataverse. https://doi.org/10.7910/DVN/5QCCUU

  29. Documenting the Now. (2020). Hydrator (version 0.0.11).

  30. Van Rossum, G., & Drake F. L. (2009). Python 3 reference manual createSpace.

  31. Peixoto, Tiago P. (2014). The graph-tool python library. Figshare.

  32. Bastian, M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. In Third international AAAI conference on weblogs and social media.

  33. Berahmand, K., Bouyer, A., & Samadi, N. (2019). A new local and multidimensional ranking measure to detect spreaders in social networks. Computing, 101, 1711–1733.

    Article  Google Scholar 

  34. Salavati, C., Abdollahpouri, A., & Manbari, Z. (2019). Ranking nodes in complex networks based on local structure and improving closeness centrality. Neurocomputing, 336, 36–45.

    Article  Google Scholar 

  35. Rui, X., Meng, F., Wang, Z., & Yuan, G. (2019). A reversed node ranking approach for influence maximization in social networks. Applied Intelligence, 49, 2684–2698.

    Article  Google Scholar 

  36. Wen, T., & Deng, Y. (2020). Identification of influencers in complex networks by local information dimensionality. Information sciences, 512, 549–562.

    Article  Google Scholar 

  37. Chen, Z. (2019). An agent-based model for information diffusion over online social networks. Papers in Applied Geography, 5, 77–97.

    Article  Google Scholar 

  38. Liu, L., Bo, Qu., Chen, B., Hanjalic, A., & Wang, H. (2018). Modelling of information diffusion on social networks with applications to WeChat. Physica A: Statistical Mechanics and its Applications, 496, 318–329.

    Article  Google Scholar 

  39. Wilson, C., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2012). Beyond social graphs: User interactions in online Social networks and their implications. ACM Transactions on the Web. https://doi.org/10.1145/2382616.2382620

    Article  Google Scholar 

  40. Mislove, Alan, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proceedings of the ACM SIGCOMM Internet Measurement Conference, (pp. 29–42) IMC.

  41. Newman, M. E. J., & Girvan, M. (2003). Finding and evaluating community structure in networks. Physics Review, 69, 1–16.

    Google Scholar 

  42. Allcott, H., Karlan, D., Möbius, M. M., Rosenblat, T. S., & Szeidl, A. (2007). Community size and network closure. The American economic review, 97, 80–85.

    Article  Google Scholar 

  43. R: Measures of network closure. (2023). https://search.r-project.org/CRAN/refmans/migraph/html/closure.html. Accessed June 20.

  44. Himelboim, I. (2017). Social network analysis (Social Media). In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), The International Encyclopedia of Communication Research Methods (pp. 1–15). John Wiley & Sons. https://doi.org/10.1002/9781118901731.iecrm0236

  45. Santoro, N., Quattrociocchi W., Flocchini P., Casteigts A., & Amblard F. (2011). Time-varying graphs and social network analysis: Temporal indicators and metrics. In AISB 2011: Social networks and multiagent systems, (pp. 33–38).

  46. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.

    Article  Google Scholar 

  47. Bulmer, M. G. (1979). Principles of Statistics. Courier Corporation.

  48. igraph R manual pages. (2023). https://igraph.org/r/html/1.3.4/transitivity.html. Accessed June 24.

Download references

Funding

This work was supported by Narodowe Centrum Nauki (National Science Centre, Poland) under Grant 2020/38/A/HS6/00066.

Author information

Authors and Affiliations

  1. Management in Networked and Digital Societies (MINDS) Department, Kozminski University, Warsaw, Poland, 03-301, 57/59 Jagiellonska St

    Amin Mahmoudi, Dariusz Jemielniak & Leon Ciechanowski

  2. MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leon Ciechanowski

Authors
  1. Amin Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dariusz Jemielniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leon Ciechanowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Mahmoudi.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics approval

The authors declare that there is no any human, animals or live human cells involved in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmoudi, A., Jemielniak, D. & Ciechanowski, L. Characteristics of two polarized groups in online social networks’ controversial discourse. J Comput Soc Sc 8, 22 (2025). https://doi.org/10.1007/s42001-024-00350-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42001-024-00350-y

Keywords

Search

Navigation