The analysis used the public Key Propagation Graph Generator/CUHK Weibo propagation corpus, assembled from parent–child records between November 2019 and March 2022. The full catalog contained approximately 4,174 source events, represented by roughly 960,000 microblogs, including reposts and comments, in the dataset authors’ Weibo22 release. Researchers excluded trees with fewer than 20 or more than 250 nodes, leaving 1,013 eligible cascades. They reconstructed directed trees from repost relationships, then calculated each event’s maximum breadth-first-search depth and its relative breadth, defined as the number of direct recipients of the seed divided by the cascade’s total size. These two structural features were standardized and separated with two-means clustering, yielding a silhouette coefficient of 0.513.

Before sampling, the clustering procedure assigned 399 events to a deep–narrow group and 614 to a shallow–broad group. The researchers then drew a balanced sample of 40 cascades from each regime, stratifying by cascade-size tertiles to reduce the chance that one size range would dominate the comparison. The resulting dataset represented about 7.9% of eligible events and contained 6,527 nodes connected by 6,447 directed repost edges. The sample was therefore designed for contrasting cascade structures, but it cannot establish the natural prevalence of either regime on Weibo. The 20–250-node eligibility window was selected before analysis to avoid near-degenerate small trees and very large events whose breadth-first-search truncation could distort depth and breadth measures.

Two prespecified hypotheses received confirmatory support. The first predicted that deep–narrow cascades would contain more opportunities for sequential, chain-relayed influence, reflected in longer average path lengths. The observed mean was 3.235 for deep–narrow events and 2.218 for shallow–broad events. A Welch two-sample test with Holm–Bonferroni adjustment produced an adjusted p-value below 0.001 and Cohen’s d of 2.71, indicating an exceptionally large separation. The second hypothesis predicted greater modularity in deep–narrow cascades, consistent with fragmented clusters in which locally connected users might encounter repeated peer endorsement. Mean modularity was 0.551 versus 0.200, also with an adjusted p-value below 0.001 and Cohen’s d of 2.95. These were effects on network geometry, not direct measurements of the psychological processes that might accompany the geometry.

Not every network measure distinguished the regimes. Average density was 0.0177 in deep–narrow cascades and 0.0231 in shallow–broad cascades, a difference that did not remain significant after multiplicity correction. Global clustering was effectively zero in both groups because parent–child repost trees rarely contain closed triangles. The selective result matters: the contrast was not a generic consequence of one group containing denser or more tightly interconnected networks, but centered on the distance between users and the separation of local communities. The authors retained density and clustering as confirmatory completeness checks rather than treating every structural statistic as evidence of a different influence regime.

Hop-by-hop analysis showed that the structural contrast developed beyond the initial burst of sharing. Shallow–broad cascades had more direct recipients of the seed, with a mean out-degree of 62.3 compared with 49.4 for deep–narrow cascades. Yet the pattern reversed at the next layer: deep–narrow nodes had a mean out-degree of 0.516, compared with 0.124 in shallow–broad cascades. At hop two, deep–narrow cascades contained 1,019 participating nodes, compared with 308 in shallow–broad cascades; at hop three, the counts were 330 and 64. By hop four, only seven shallow–broad nodes remained, while 131 deep–narrow nodes were still active. Deep–narrow events continued, with declining counts, through hops five to nine, whereas shallow–broad events effectively ended by hop five. These profiles describe continued opportunities for local influence, not a measured trajectory of anxiety, emotion, persuasion or uncertainty reduction.

An exploratory analysis also found higher betweenness centrality in deep–narrow cascades across all three branching-role proxies. Mean values were 0.003781 versus 0.000216 for high-branching nodes, 0.003215 versus 0.001377 for medium-branching nodes, and 0.004603 versus 0.000722 for low-branching nodes. The roles were structural classifications based on out-degree, not verified categories of “influencers,” media outlets or ordinary users. Because these comparisons were not included in the multiplicity-controlled confirmatory family, the result is treated as hypothesis-generating evidence that deep–narrow networks may distribute brokerage across several classes of nodes rather than concentrate it in one identifiable group. The interpretation remains descriptive and does not establish that a particular account or class of users controlled the flow of information.

Together, path length, modularity and hop-layer branching offer a meso-level picture of how sharing behavior is organized. In deep–narrow cascades, separated modules coexist with elongated relay chains, creating multiple locations where information can pass between branches and where local social proof could potentially accumulate. In shallow–broad cascades, many recipients appear close to the seed or to early distributors, producing a compact pattern dominated by early broadcast exposure. The study therefore distinguishes opportunities for psychological processes from proof that those processes occurred. No questionnaire, self-report, emotion coding, personality measure or attitude assessment was available to show whether users actually felt uncertain, sought reassurance or changed their views. The researchers also did not use the corpus’s text features to score emotion, topic or veracity.

Several limitations constrain the conclusions. The analysis is observational, so topology may reflect unmeasured factors such as the seed account’s characteristics, topic, timing, audience size, platform recommendations, external events, temporal change across 2019–2022 or other factors unavailable in the edge-list archive. The data also span years that may include changes in Weibo’s platform environment, and the two-cluster solution was not presented as a uniquely natural taxonomy; alternative clustering features, size windows or numbers of clusters could shift membership at the margin. Average path length is mathematically related to depth, one of the features used to define the regimes, so the large contrast partly characterizes the clusters rather than independently validating them. Although a continuous regression related path length to depth and breadth with an R² of 0.756, that relationship likewise cannot establish a causal psychological mechanism or prove that deep–narrow cascades were more emotional, persuasive or truthful.

For media psychology, the study’s principal advance is a measurable middle layer between isolated forwarding choices and platform-wide outcomes such as virality. Cascade geometry can be treated as a set of indicators for future research: path length can represent sequential opportunity, modularity can capture local clustering, betweenness can describe brokerage and hop-layer branching can track where diffusion opportunities persist. Pairing those structural measures with participant surveys, coded content, personality data or experimental attitude measures could test whether users experience the two regimes differently. The current evidence supports a narrower conclusion: within this selected observational sample of Weibo cascades, observed sharing aggregated into at least two structurally distinct influence regimes—one built around extended, modular chains and the other around early, hub-centered exposure—without demonstrating the intrapersonal psychological mechanisms behind either structure.

Subject of Research: Observational analysis of structural regimes in Weibo repost cascades

Article Title: Social influence aggregation on Weibo: psychological process expectations and meso-level cascade regimes in media psychology

Article References: Wang, L. (2026). Social influence aggregation on Weibo: psychological process expectations and meso-level cascade regimes in media psychology. Frontiers in Psychology, 17, Article 1953616. https://doi.org/10.3389/fpsyg.2026.1953616

Image Credits: AI Generated

DOI: 10.3389/fpsyg.2026.1953616

Keywords: Weibo, social influence, information cascades, media psychology, network topology, reposting, online diffusion, social proof, digital media, Social, influence, aggregation