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arxiv: 2604.05800 · v1 · submitted 2026-04-07 · 💻 cs.CY · cs.SI

Conditional Publics: Shared Events and Divergent Meanings in the European Twitter Debate on the Ukraine War

Corrado Monti , Arthur Capozzi , Yelena Mejova , Gianmarco De Francisci Morales This is my paper

Pith reviewed 2026-05-10 18:52 UTC · model grok-4.3

classification 💻 cs.CY cs.SI
keywords social mediapublic spherepolarizationUkraine warTwitteropinion clustersstance analysisconditional publics
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The pith

European Twitter users form conditional publics on the Ukraine war, sharing events on pragmatist issues but diverging on interpretive ones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines millions of geolocated tweets from twenty European countries in the early months of the Russian invasion to map how publics form around the conflict. It finds consistent hawkish and doveish opinion clusters in nearly every country, with structural polarization resulting mainly from casual users leaving the conversation. On pragmatist issues both clusters respond to the same high-profile events and maintain a shared frame, while on interpretive issues they build separate meanings and function as counterpublics. The authors introduce conditional publics to capture how these formations either align or split depending on the epistemic character of each issue.

Core claim

Retweet community detection and stance annotation across six issues reveal hawkish and doveish clusters present in almost every one of the twenty countries. On pragmatist issues the two clusters orient toward the same events and sustain an agonistic public sphere. On interpretive issues they operate as affective publics and counterpublics that construct divergent meanings. The relational structure of these publics is therefore conditional on the epistemic character of the debated issue rather than fixed by national or ideological boundaries.

What carries the argument

Conditional publics: formations whose sharing or fracturing of a referential frame depends on the epistemic character of the debated issue.

Load-bearing premise

That the six issues can be validly classified into pragmatist versus interpretive categories and that retweet communities plus stance annotations reliably reflect stable opinion clusters rather than artifacts of data collection or labeling.

What would settle it

Finding that the two clusters mention different high-profile events even on issues labeled pragmatist, or that consistent hawkish and doveish clusters fail to appear across most countries when the same methods are applied.

Figures

Figures reproduced from arXiv: 2604.05800 by Arthur Capozzi, Corrado Monti, Gianmarco De Francisci Morales, Yelena Mejova.

Figure 1
Figure 1. Figure 1: Dynamics of the Twitter debate on the Ukraine war over time. (Top) The total daily number of retweets (red), [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Community-level stances and temporal clustering. On the left, the dendrogram displays the hierarchical clustering [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Temporal dynamics of the political positions expressed by the polarizing retweet activity in each country. The [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pairwise Spearman correlation networks of Hawkish and Doveish sides across countries and axes (meaning of [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of pairwise Spearman correlations for country pairs sharing the same data language (green) versus [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Number of tweets corresponding to the shared attention peaks by each side (red/blue), whether they are related [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

How do European publics debate a geopolitical crisis on social media, and do they inhabit a shared informational reality? We analyze over 38 million geolocated tweets from 20 European countries during the first eight months of the Russian invasion of Ukraine. Using retweet community detection and stance annotation across six issues, we identify 'hawkish' and 'doveish' opinion clusters present within almost every country studied. We find that structural polarization is driven not by radicalization, but by the exit of casual users. Crucially, whether opposing sides orient to the same events depends on the issue. On pragmatist issues, both sides react to the same high-profile events, forming an agonistic public sphere. Instead, on interpretive issues, they operate as affective publics and counterpublics constructing divergent meanings. We propose conditional publics to describe formations whose relational structure, sharing or fracturing a referential frame, depends on the epistemic character of the debated issue.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript analyzes over 38 million geolocated tweets from 20 European countries in the first eight months of the Russian invasion of Ukraine. Using retweet community detection and stance annotation across six issues, it identifies consistent 'hawkish' and 'doveish' opinion clusters within nearly every country. The authors argue that structural polarization arises from the exit of casual users rather than radicalization. They report that on pragmatist issues both sides orient to the same high-profile events (forming an agonistic public sphere), while on interpretive issues they operate as affective publics and counterpublics constructing divergent meanings. The central theoretical contribution is the proposal of 'conditional publics,' whose relational structure (shared or fractured referential frame) depends on the epistemic character of the debated issue.

Significance. If the empirical patterns and classification hold, the work makes a substantive contribution to computational social science and political communication by showing that online public formation during geopolitical crises is issue-dependent rather than uniform. The scale of the cross-national dataset and the differentiation between shared-event alignment and meaning divergence provide empirical leverage on theories of agonistic versus affective publics. Credit is due for the large-scale data collection and the attempt to link epistemic issue types to observable network and stance structures. The significance is tempered by the need for greater methodological transparency on validation and classification criteria.

major comments (3)
  1. [§4 (Issue Selection and Classification)] The classification of the six issues into pragmatist versus interpretive categories is load-bearing for the 'conditional publics' claim (§4 and §5). The manuscript does not provide explicit a priori operational criteria, coding rules, or inter-coder reliability statistics for this distinction. Without pre-specified criteria independent of the retweet and stance patterns, the finding that publics share frames on pragmatist issues but fracture on interpretive ones risks circularity: the categories may be fitted to the observed divergence rather than tested against it. A concrete fix would be to report the classification protocol and any robustness checks performed before presenting the event-sharing results.
  2. [Methods and §5.1] Validation details for stance annotation and retweet community detection are insufficient to support the cross-country stability of hawkish/doveish clusters (Methods section and §5.1). The abstract and main text lack inter-annotator agreement scores, robustness tests to annotation guidelines or model parameters, and sensitivity analyses for community detection resolution. These steps are central because the claim that polarization is driven by casual-user exit (rather than radicalization) and the issue-dependent alignment rest on the clusters being reliable rather than artifacts of labeling or parameter choice.
  3. [Results (event sharing subsection)] The measurement of 'shared events' versus 'divergent meanings' is not fully specified (Results, event-alignment analysis). It is unclear how temporal co-occurrence, semantic similarity, or referential frame sharing was quantified across the two issue types, and whether this was done with pre-registered thresholds or post-hoc observation. This directly affects the contrast between agonistic and affective publics and requires explicit operationalization to allow replication.
minor comments (2)
  1. [Figures 1-4] Figure captions and legends should explicitly state the exact number of tweets, countries, and time window used in each panel to improve reproducibility.
  2. [Introduction and §2] The term 'conditional publics' is introduced in the abstract and conclusion but would benefit from a concise formal definition in the theoretical section to distinguish it from related concepts such as issue publics or counterpublics.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important areas for improving methodological transparency in our manuscript. We have revised the paper to address each of the major comments by adding explicit protocols, validation details, and operational specifications. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [§4 (Issue Selection and Classification)] The classification of the six issues into pragmatist versus interpretive categories is load-bearing for the 'conditional publics' claim (§4 and §5). The manuscript does not provide explicit a priori operational criteria, coding rules, or inter-coder reliability statistics for this distinction. Without pre-specified criteria independent of the retweet and stance patterns, the finding that publics share frames on pragmatist issues but fracture on interpretive ones risks circularity: the categories may be fitted to the observed divergence rather than tested against it. A concrete fix would be to report the classification protocol and any robustness checks performed before presenting the event-sharing results.

    Authors: We agree that explicit a priori criteria are necessary to substantiate the conditional publics argument and prevent any risk of circularity. The six issues were selected for their prominence in the debate and classified prior to network and stance analyses according to whether they center on concrete policy actions and verifiable events (pragmatist) or on subjective interpretations, historical framing, and value-based judgments (interpretive), consistent with distinctions in political communication literature. In the revised manuscript we have added a dedicated subsection in §4 that reports the full classification protocol, coding rules, and definitions applied to each issue. We also include inter-coder reliability statistics from independent annotation and robustness checks demonstrating that the core patterns of event sharing versus meaning divergence hold under alternative classifications. revision: yes

  2. Referee: [Methods and §5.1] Validation details for stance annotation and retweet community detection are insufficient to support the cross-country stability of hawkish/doveish clusters (Methods section and §5.1). The abstract and main text lack inter-annotator agreement scores, robustness tests to annotation guidelines or model parameters, and sensitivity analyses for community detection resolution. These steps are central because the claim that polarization is driven by casual-user exit (rather than radicalization) and the issue-dependent alignment rest on the clusters being reliable rather than artifacts of labeling or parameter choice.

    Authors: We acknowledge that the original Methods section lacked sufficient validation detail. In the revision we have substantially expanded this section to report inter-annotator agreement scores for the stance annotation task, the complete annotation guidelines provided to coders, and robustness tests that vary annotation model parameters. For retweet community detection we now include sensitivity analyses across a range of resolution parameters in the community detection algorithm, confirming that the hawkish/doveish cluster structure remains stable across nearly all countries. These additions directly support the reliability of the polarization mechanism and the issue-dependent alignment findings. revision: yes

  3. Referee: [Results (event sharing subsection)] The measurement of 'shared events' versus 'divergent meanings' is not fully specified (Results, event-alignment analysis). It is unclear how temporal co-occurrence, semantic similarity, or referential frame sharing was quantified across the two issue types, and whether this was done with pre-registered thresholds or post-hoc observation. This directly affects the contrast between agonistic and affective publics and requires explicit operationalization to allow replication.

    Authors: We agree that the operationalization of shared events versus divergent meanings must be fully specified for replicability. In the revised Results section we now explicitly describe the quantification procedure: temporal co-occurrence is measured by aligning spikes in tweet volume around documented high-profile events, combined with semantic similarity computed via sentence embeddings to assess referential frame sharing. We clarify how thresholds were determined and applied consistently, distinguishing the two issue types. A new appendix supplies the complete list of events, the similarity computation details, and replication code to enable independent verification of the agonistic versus affective public distinction. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical derivation of conditional publics

full rationale

The paper's analysis relies on processing 38 million geolocated tweets via retweet community detection and stance annotation across six issues. The pragmatist/interpretive distinction is applied to interpret observed differences in event orientation and meaning construction, with the 'conditional publics' concept offered as a descriptive framing of those patterns. No load-bearing steps reduce to self-defined fitted quantities, self-citation chains, or ansatzes smuggled via prior work. The derivation chain is self-contained empirical observation rather than a closed loop where outputs are forced by construction from inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The work relies on standard assumptions from computational social science about network communities and content labels. The main addition is the conceptual category conditional publics without external validation.

axioms (2)
  • domain assumption Retweet communities correspond to distinct opinion stances (hawkish vs doveish)
    Used to identify clusters within countries from the retweet graph.
  • domain assumption Issues can be categorized as pragmatist or interpretive based on their epistemic character
    Central to explaining differences in whether sides orient to the same events.
invented entities (1)
  • conditional publics no independent evidence
    purpose: Describes opinion formations whose sharing or fracturing of events depends on the epistemic type of the issue
    New term introduced to unify the observed patterns of agonistic vs affective public behavior.

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Reference graph

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