Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2402.03385

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

Pilar Marqués-Sánchez·María Cristina Martínez-Fernández·José Alberto Benítez-Andrades·Enedina Quiroga-Sánchez·María Teresa García-Ordás·Natalia Arias-Ramos
View original source ↗Read source document ↗Export metadata ↓

Abstract

Aim: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. Methods: 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. Results: After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Conclusion: Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Explore related subjects

cs.SIcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pilar Marqués-Sánchez, María Cristina Martínez-Fernández, José Alberto Benítez-Andrades, Enedina Quiroga-Sánchez, María Teresa García-Ordás, Natalia Arias-Ramos. 2024-02-04. Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques. https://doi.org/10.1371/journal.pone.0289553

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Benefit of Collective Intelligence in Community-Based Content Moderation is Limited by Overt Political Signalling

Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Community Notes on Meta, and Footnotes on TikTok. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesise that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance the overall quality of their notes. To test this idea, we conducted an online experiment in which participants jointly authored notes on politically misleading posts. We find that collaboration improves the helpfulness of notes, although the average effect depends on the interactional context. In particular, the benefits of collaboration decline when participants are made aware of one another's political affiliations. We also find that politically diverse teams improve note quality when evaluating Republican posts, while team composition does not meaningfully affect note quality for Democrat posts. These findings underscore the complexity of community-based content moderation and highlight the importance of understanding group dynamics and political diversity when designing more effective moderation systems.

cs.SI↗
arXiv2026

The Same Ledger, Different Verdicts: How Measurement Specification Determines On-Chain Concentration

Whether a public blockchain is "decentralized" is routinely settled by citing a concentration statistic. On two ERC-20 ledgers, Chainlink (LINK) and Uniswap (UNI) over a 90-day window, we show that verdict depends on measurement specification rather than the ledger itself. Four discretionary choices (holder population, address type, temporal aggregation, and entity resolution) move the balance HHI for UNI from 109 to 2,336 (a factor of 21), with every specification defensible. Over the same range, the Gini coefficient moves by less than 0.003 and does not change under entity resolution, demonstrating that Gini and HHI answer different questions and cannot substitute for one another. We further document an implementation choice - summing versus overwriting repeated transfers - that discards roughly 85% of volume and overturns a finding on wealth and structural position. Substantively, both ledgers are extraordinarily unequal in ownership (balance Gini = 0.990 and 0.998) yet unconcentrated in routing (weekly flow HHI = 421 and 386), with the two dimensions close to statistically independent across addresses. A parameterized criterion for hidden brokers identifies 30 and 18 zero-balance intermediaries, 12 shared across ledgers; a matched control confirms that degree thresholding, rather than learned embeddings, drives the discovery. Finally, on the governance ledger, proposal-eligible addresses and routing intermediaries are almost disjoint, so routing contestability is held at the pleasure of a rule layer with a Nakamoto coefficient of two. We conclude that on-chain concentration should be reported as a specified range rather than a point estimate.

cs.SI↗
arXiv2026

Optimal and heuristic strategies for evaluating the influence of coordinated behavior in information cascades and retweet networks

Coordinated Inauthentic Behavior (CIB) has become a major concern in online social platforms, yet its actual impact on information diffusion remains poorly understood. Existing research has primarily focused on detecting coordinated activity, while comparatively little attention has been devoted to quantifying its influence once detected. In this work, we introduce two complementary frameworks for the post-hoc evaluation of coordinated accounts. First, we formulate the problem on information cascades as a constrained influence maximization problem over directed trees and develop a polynomial-time dynamic programming algorithm that computes the optimal placement of coordinated nodes, providing an upper bound on their achievable influence. Second, motivated by the limited availability of diffusion cascades in real-world platforms, we propose a network-based framework that estimates influence directly from retweet networks using the independent cascade model and compares the observed placement of coordinated accounts against established heuristic baselines. We evaluate both approaches on Twitter/X data from the 2019 UK General Election and on a collection of verified state-backed information operation campaigns spanning multiple countries. While coordinated accounts exhibit limited influence in the UK cascades, the network-based analysis reveals substantial differences across campaigns, with several operations achieving influence comparable to or exceeding that of structurally central seed sets. Finally, by reconstructing cascades from the retweet networks, we show that the two frameworks produce consistent results, suggesting that the observed effects reflect intrinsic structural properties of coordinated activity rather than artifacts of the underlying methodology.

cs.SI↗