Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Who is driving the conversation? Analysing the nodality of British MPs and journalists on social media (2402.08765v3)

Published 13 Feb 2024 in cs.SI and stat.AP

Abstract: With the rise of social media, political conversations now take place in more diffuse environments. In this context, it is not always clear why some actors, more than others, have greater influence on how discussions are shaped. To investigate the factors behind such influence, we build on nodality, a concept in political science which describes the capacity of an actor to exchange information within discourse networks. This concept goes beyond traditional network metrics that describe the position of an actor in the network to include exogenous drivers of influence (e.g. factors relating to organisational hierarchies). We study online discourse on Twitter (now X) in the UK to measure the relative nodality of two sets of policy actors - Members of Parliament (MPs) and accredited journalists - on four policy topics. We find that influence on the platform is driven by two key factors: (i) active nodality, derived from the actor's level of topic-related engagement, and (ii) inherent nodality, which is independent of the platform discourse and reflects the actor's institutional position. These findings significantly further our understanding of the origins of influence on social media platforms and suggest in which contexts influence is transferable across topics.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (69)
  1. Hate speech in political discourse: A case study of UK MPs on twitter. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, HT ’21, pages 5–16. Association for Computing Machinery.
  2. Measuring influence on Twitter. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, pages 1–4, Graz Austria. ACM.
  3. k-means++: the advantages of careful seeding. In SODA ’07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027–1035, New Orleans, Louisiana. Society for Industrial and Applied Mathematics.
  4. Scalable k-means++. Proceedings of the VLDB Endowment, 5(7):622–633.
  5. Everyone’s an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11, pages 65–74, New York, NY, USA. Association for Computing Machinery.
  6. Comparative studies of policy agendas. Journal of European Public Policy, 13(7):959–974. Publisher: Routledge _eprint: https://doi.org/10.1080/13501760600923805.
  7. Comparative Studies of Policy Agendas. Routledge. Google-Books-ID: JbraAAAAQBAJ.
  8. Increasing sentence-level comprehension through text classification of epistemic functions. null.
  9. Berkowitz, D. (1992). Who sets the media agenda? the ability of policymakers to determine news decisions. Public opinion, the press, and public policy, 2:81–102.
  10. Inequality and the COVID-19 Crisis in the United Kingdom. Annual Review of Economics, 14(1):607–636. _eprint: https://doi.org/10.1146/annurev-economics-051520-030252.
  11. Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1):7. Number: 1 Publisher: Nature Publishing Group.
  12. Bright, J. (2016). The Social News Gap: How News Reading and News Sharing Diverge. Journal of Communication, 66(3):343–365. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcom.12232.
  13. 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.
  14. Does Campaigning on Social Media Make a Difference? Evidence From Candidate Use of Twitter During the 2015 and 2017 U.K. Elections. Communication Research, 47(7):988–1009. Publisher: SAGE Publications Inc.
  15. The Nodality Disconnect of Data-Driven Government. Administration & Society, 53(9):1418–1442. Publisher: SAGE Publications Inc.
  16. Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data. ArXiv. arXiv:2401.12295 [cs].
  17. Chadha, J. S. (2022). MONETARY AND FISCAL POLICY REDUX—THE MINI-BUDGET. National Institute Economic Review, 262:1–7.
  18. Expecting Brexit. Annual Review of Economics, 14(1):495–519. _eprint: https://doi.org/10.1146/annurev-economics-051420-104231.
  19. Diamond, P. (2023). Post-Brexit challenges to the UK machinery of government in an ‘Age of Fiasco’: the dangers of muddling through? Journal of European Public Policy, 0(0):1–24. Publisher: Routledge _eprint: https://doi.org/10.1080/13501763.2023.2225562.
  20. The Multiple Facets of Influence: Identifying Political Influentials and Opinion Leaders on Twitter. American Behavioral Scientist, 58(10):1260–1277. Publisher: SAGE Publications Inc.
  21. New method to measure the influence of Twitter users. In 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), pages 1–5.
  22. Leaders or brokers? potential influencers in online parliamentary networks. Policy & Internet, 10(1):61–86. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/poi3.150.
  23. Rising cost of living in the UK. House of Commons Library.
  24. Ontology-driven weak supervision for clinical entity classification in electronic health records. Nature Communications, 12(1):2017. Number: 1 Publisher: Nature Publishing Group.
  25. Which politicians receive abuse? four factors illuminated in the UK general election 2019. EPJ Data Science, 9.
  26. The role of social media in local government crisis communications. Public Relations Review, 41(3):386–394.
  27. Between Broadcasting Political Messages and Interacting with Voters. Information, Communication & Society, 16(5):692–716. Publisher: Routledge _eprint: https://doi.org/10.1080/1369118X.2013.785581.
  28. The supply and demand of news during COVID-19 and assessment of questionable sources production. Nature Human Behaviour, 6(8):1069–1078. Number: 8 Publisher: Nature Publishing Group.
  29. Hood, C. C. (1983). The Tools of Government. Macmillan Education UK, London.
  30. The Tools of Government in the Digital Age. Palgrave Macmillan.
  31. Howlett, M. (2009). Government Communication as a Policy Tool: A Framework for Analysis. Canadian Political Science Review, 3(2):23–37. Number: 2.
  32. Howlett, M. (2022). Information as a Policy Tool: The Role of Government Communication. In The Routledge Handbook of Policy Tools. Routledge. Num Pages: 11.
  33. Jain, N. (2021). Customer sentiment analysis using weak supervision for customer-agent chat. arXiv: Computation and Language.
  34. Information Flows? A Critique of Transfer Entropies. Physical Review Letters, 116(23):238701.
  35. Principal component analysis: a review and recent developments. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2065):20150202.
  36. Jungherr, A. (2014). Twitter in Politics: A Comprehensive Literature Review.
  37. A local search approximation algorithm for k-means clustering. Computational Geometry, 28(2):89–112.
  38. Measuring Twitter-based political participation and deliberation in the South Korean context by using social network and Triple Helix indicators. Scientometrics, 90(1):121–140.
  39. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632.
  40. Li, Y. Y. (2023). Britain’s economic crisis in Mini-budget perspective. Academic Journal of Management and Social Sciences, 2(1):1–5. Number: 1.
  41. Economic costs of the Russia-Ukraine war. The World Economy, 46(4):874–886. Publisher: John Wiley & Sons, Ltd.
  42. Extracting chemical reactions from text using snorkel. BMC Bioinformatics.
  43. Ontoaugment: Ontology matching through weakly-supervised label augmentation. null.
  44. Political Turbulence: How Social Media Shapes Political Participation and the Democratic Landscape. In Graham, M. and Dutton, W. H., editors, Society and the Internet: How Networks of Information and Communication are Changing Our Lives, page 0. Oxford University Press.
  45. How rediscovering nodality can improve democratic governance in a digital world. Public Administration, n/a(n/a). _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/padm.12960.
  46. Governing from the Centre? Comparing the Nodality of Digital Governments.
  47. Russian-Ukraine 2022 War: A Review of the Economic Impact of Russian-Ukraine Crisis on the USA. Advances in Social Sciences Research Journal, 9:144–153.
  48. The Press. Oxford University Press. Google-Books-ID: fnLiBwAAQBAJ.
  49. Finding and Analyzing Principal Features for Measuring User Influence on Twitter. In 2015 IEEE First International Conference on Big Data Computing Service and Applications, pages 478–486.
  50. Measuring exposure to misinformation from political elites on Twitter. Nature Communications, 13(1):7144. Number: 1 Publisher: Nature Publishing Group.
  51. Total Jensen divergences: Definition, Properties and k-Means++ Clustering. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2016–2020. arXiv:1309.7109 [cs, math].
  52. “Birds of a Feather Flock Together” : Bipolarity, Homophily and Intra-Party Communication Degree of Polish MPs on Twitter. Athenaeum Polskie Studia Politologiczne, 72(4):7–28.
  53. Parmelee, J. H. (2013). Political journalists and Twitter: Influences on norms and practices. Journal of Media Practice, 14(4):291–305. Publisher: Routledge _eprint: https://doi.org/10.1386/jmpr.14.4.291_1.
  54. The web structure of e-government - developing a methodology for quantitative evaluation. In Proceedings of the 15th international conference on World Wide Web, WWW ’06, pages 669–678, New York, NY, USA. Association for Computing Machinery.
  55. Training complex models with multi-task weak supervision. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 33:4763–4771.
  56. Measuring time-sensitive user influence in Twitter. Knowledge and Information Systems, 62(9):3481–3508.
  57. Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5):949–975.
  58. Schreiber, T. (2000). Measuring Information Transfer. Physical Review Letters, 85(2):461–464. Publisher: American Physical Society.
  59. Politicians and the Policy Agenda: Does Use of Twitter by the U.S. Congress Direct New York Times Content? Policy and Internet, 9:109–132.
  60. Nodal Governance, Democracy, and the New ‘Denizens’. Journal of Law and Society, 30(3):400–419. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-6478.00263.
  61. Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data. PLOS Computational Biology, 17(4):e1008054. Publisher: Public Library of Science.
  62. Early detection of fake news with multi-source weak social supervision. null.
  63. Interpretable risk models for sleep apnea and coronary diseases from structured and non-structured data. Expert Systems With Applications.
  64. An actor-based approach to understanding radical right viral tweets in the UK. Journal of Policing, Intelligence and Counter Terrorism, 0(0):1–19. Publisher: Routledge _eprint: https://doi.org/10.1080/18335330.2022.2086440.
  65. Growth … What growth? Finance Research Letters, 52:103594.
  66. Sticks and stones: Comparing Twitter campaigning strategies in the European Union referendum. The British Journal of Politics and International Relations. Publisher: SAGE PublicationsSage UK: London, England.
  67. Recalibrating classifiers for interpretable abusive content detection. In Bamman, D., Hovy, D., Jurgens, D., O’Connor, B., and Volkova, S., editors, Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 132–138. Association for Computational Linguistics.
  68. How do issues get on public policy agendas? In Sociology and the Public Agenda, pages 40–50. SAGE Publications, Inc.
  69. An exploratory study of Twitter metrics for measuring user influence. Journal of Informetrics, 17(4):101454.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now