Who is driving the conversation? Analysing the nodality of British MPs and journalists on Twitter
<|reference_start|>Who is driving the conversation? Analysing the nodality of British MPs and journalists on Twitter: Who sets the policy agenda? In this paper, we explore the roles of policy actors in agenda setting by studying their relative influence in policy-related discussions. Our approach builds on ``nodality'' \textemdash a concept in political science that determines the capacity of an actor to share information and to be at the centre of information networks. We propose a novel methodology that quantifies the nodality of all individual actors in any conversation by analysing a comprehensive set of their centrality measures in the related information network. We combine this with the analysis of the activity time-series, of the related conversation (or topic), to demonstrate how nodality scores relate to the capacity to drive topic-related activity. Here we analyse policy-related discussions on X (previously Twitter) and quantify the nodality of two sets of actors in the UK political system \textemdash Members of Parliament (MPs) and accredited journalists - on four policy topics: The Russia-Ukraine War, the Cost-of-Living Crisis, Brexit and COVID-19. Our results show that the capacity to influence the activity related to a topic is significantly and positively associated with nodality. In particular, we identify two dimensions of nodality that drive the capacity to influence topic-related activity. The first is ``active nodality", which reflects the level of topic-related engagement an individual actor has on the platform. The second dimension is ``inherent nodality" which is entirely independent of the platform and reflects the actor's institutional position (such as an MP in a front-bench role, or a journalist's position at a prominent media outlet).<|reference_end|>
@article{castro-gonzalez2024who,
    title={Who is driving the conversation? Analysing the nodality of British MPs
  and journalists on Twitter},
    author={Leonardo Castro-Gonzalez and Sukankana Chakraborty and Helen Margetts
  and Hardik Rajpal and Daniele Guariso and Jonathan Bright},
    journal={arXiv preprint arXiv:2402.08765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08765},
    primaryClass={cs.SI stat.AP}
}