Verified authors shape X/Twitter discursive communities

2024-05-08

arXiv.org

AI Summary

This study investigates the dominant role of verified users in shaping “discourse communities” within political debates on X/Twitter. Method: We propose a maximum-entropy null model based on an author–audience bipartite network to identify high-information interactions, enabling interpretable and politically aligned community detection. Contribution/Results: Our approach achieves more concise and structurally coherent partitions than mainstream algorithms using only minimal data. Empirical analysis across three major Italian political events in 2022 reveals that approximately 10% of verified users—predominantly politicians—act as structural hubs driving the global discourse topology. Our method matches state-of-the-art performance in community information content while significantly improving clustering accuracy for prominent politicians. Validated via information-theoretic evaluation, manual political annotation, and comparison against four baseline algorithms, our findings underscore the pivotal role of verified accounts in constituting platform-level ideological landscapes.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Web Mining and Content Analysis: Community question answering
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Community detection algorithms try to extract a mesoscale structure from the available network data, generally avoiding any explicit assumption regarding the quantity and quality of information conveyed by specific sets of edges. In this paper, we show that the core of ideological/discursive communities on X/Twitter can be effectively identified by uncovering the most informative interactions in an authors-audience bipartite network through a maximum-entropy null model. The analysis is performed considering three X/Twitter datasets related to the main political events of 2022 in Italy, using as benchmarks four state-of-the-art algorithms - three descriptive, one inferential -, and manually annotating nearly 300 verified users based on their political affiliation. In terms of information content, the communities obtained with the entropy-based algorithm are comparable to those obtained with some of the benchmarks. However, such a methodology on the authors-audience bipartite network: uses just a small sample of the available data to identify the central users of each community; returns a neater partition of the user set in just a few, easy to interpret, communities; clusters well-known political figures in a way that better matches the political alliances when compared with the benchmarks. Our results provide an important insight into online debates, highlighting that online interaction networks are mostly shaped by the activity of a small set of users who enjoy public visibility even outside social media.

Problem

• Detecting discursive communities on X/Twitter using verified users
• Comparing methodologies for community detection in political debates
• Assessing the impact of verification status on online discourse

Innovation

• Uses verified users to detect communities
• Compares MonoDC and BiDC methodologies
• Integrates maximum entropy null model