IDEAS home Printed from https://ideas.repec.org/a/kap/policy/v58y2025i1d10.1007_s11077-024-09553-6.html
   My bibliography  Save this article

Breaking away from family control? Collaboration among political organisations and social media endorsement among their constituents

Author

Listed:
  • Paul M. Wagner

    (Edinburgh Napier University)

  • Arttu Malkamäki

    (Aalto University)

  • Tuomas Ylä-Anttila

    (University of Helsinki)

Abstract

Coalitions that engage in political advocacy are constituted by organisations, which are made up of individuals and organisational subunits. Comparing the coalitions formed by organisations to the those formed by their constituent parts provides a means of examining the extent to which their coalition memberships are aligned. This paper applies inferential network clustering methods to survey data collected from organisations engaging in Irish climate change politics and to X (formerly twitter) data extracted from both the primary accounts of these organisations and the accounts of the individuals and subunits affiliated with them. Analysis of the survey-based organisation-level policy network finds evidence of an outsider coalition, formed by non-governmental organisations, labour unions and left-leaning political parties, and an insider coalition formed by the two main political parties in government, energy sector organisations, business and agricultural interests, scientific organisations, and government bodies. An analysis of the X-based account-level endorsement network finds evidence for a nested coalition structure wherein there are multiple distinct communities, which largely align with the organisation-level coalitions. Most interestingly, the largest and most active community is formed by accounts affiliated with the organisations with agricultural interests—the sector most opposed to ambitious climate action in Ireland. The results show how the somewhat disjoint behaviours of formal organisations and their affiliates give rise to nested coalitions, which can only be identified by disaggregating organisations by their constituent parts.

Suggested Citation

  • Paul M. Wagner & Arttu Malkamäki & Tuomas Ylä-Anttila, 2025. "Breaking away from family control? Collaboration among political organisations and social media endorsement among their constituents," Policy Sciences, Springer;Society of Policy Sciences, vol. 58(1), pages 27-43, March.
    • Handle: RePEc:kap:policy:v:58:y:2025:i:1:d:10.1007_s11077-024-09553-6
    DOI: 10.1007/s11077-024-09553-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11077-024-09553-6
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11077-024-09553-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Malkamäki, Arttu & Ylä-Anttila, Tuomas & Brockhaus, Maria & Toppinen, Anne & Wagner, Paul M., 2021. "Unity in diversity? When advocacy coalitions and policy beliefs grow trees in South Africa," Land Use Policy, Elsevier, vol. 102(C).
    2. Koebele, Elizabeth A., 2019. "Integrating collaborative governance theory with the Advocacy Coalition Framework," Journal of Public Policy, Cambridge University Press, vol. 39(1), pages 35-64, March.
    3. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    4. Dallas J. Elgin & Christopher M. Weible, 2013. "A Stakeholder Analysis of C olorado Climate and Energy Issues Using Policy Analytical Capacity and the Advocacy Coalition Framework," Review of Policy Research, Policy Studies Organization, vol. 30(1), pages 114-133, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aerang Nam & Christopher M. Weible & Kyudong Park, 2022. "Polarization and frames of advocacy coalitions in South Korea's nuclear energy policy," Review of Policy Research, Policy Studies Organization, vol. 39(4), pages 387-410, July.
    2. Radtke, Jörg & Scherhaufer, Patrick, 2022. "A social science perspective on conflicts in the energy transition: An introduction to the special issue," Utilities Policy, Elsevier, vol. 78(C).
    3. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    4. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    5. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    6. Nora Connor & Albert Barberán & Aaron Clauset, 2017. "Using null models to infer microbial co-occurrence networks," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    7. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Xinyi Liu & Bin Liu & Zhimin Huang & Ting Shi & Yingyi Chen & Jian Zhang, 2012. "SPPS: A Sequence-Based Method for Predicting Probability of Protein-Protein Interaction Partners," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-6, January.
    9. Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities," Data, MDPI, vol. 4(4), pages 1-23, November.
    10. Li, Qing & Zhang, Huaige & Hong, Xianpei, 2020. "Knowledge structure of technology licensing based on co-keywords network: A review and future directions," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 154-165.
    11. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    12. Linhai Wu & Liwei Zhang & Yufeng Li, 2023. "Basis for fulfilling responsibilities, behavior, and professionalism of government agencies and effectiveness in public–public collaboration for food safety risk management," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    13. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    14. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    15. Mockshell, Jonathan & Birner, Regina, 2020. "Who has the better story? On the narrative foundations of agricultural development dichotomies," World Development, Elsevier, vol. 135(C).
    16. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    17. Yao, Can-Zhong & Lin, Ji-Nan & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2015. "The study of RMB exchange rate complex networks based on fluctuation mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 359-376.
    18. Neal D. Woods, 2021. "The State of State Environmental Policy Research: A Thirty‐Year Progress Report," Review of Policy Research, Policy Studies Organization, vol. 38(3), pages 347-369, May.
    19. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    20. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:policy:v:58:y:2025:i:1:d:10.1007_s11077-024-09553-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.