Multiplex relational event models of self-reported and perceived gossip

Abstract

Gossiping is a complex social phenomenon that unfolds through multiple types of relations. Among other things, individuals may initiate gossiping about others as a result of believing themselves to be the target of gossip. These two processes, i.e., gossiping and gossip perception, form a dynamic multiplex network, where self-reported and perceived gossip ties coexist and can influence each other over time. We develop a multiplex relational event model to study the joint evolution of these two processes. The proposed model includes endogeneous covariates that capture the influence of events occurring within one layer, as well as events occurring on the other layer, on the rate of self-reported and perceived gossiping. We analyze survey data collected annually from 44 secondary schools in Hungary. We show how a multiplex relational event model, including linear, smooth and random effects, is able to describe the social mechanisms operating within and between self-reported and perceived gossiping and to capture how these two layers influence each other over time.

Refining Relational Event Models: Bayesian Penalization and Variable Selection in REMs
Jonathan Koop, Sara van Erp, Mahdi Shafiee Kamalabad
arXiv28 Aug 2026PreprintCC BY-SA 4.0Full textStatisticsarXiv
Perceived factors associated with self-reported AI-assisted academic cheating among undergraduate students: Evidence from ethical ambiguity, technological affordances, institutional gaps, academic pressure, and peer comparison
Hanh Van Nguyen, Mai Thi-Thuy Duong
PLOS One28 Sep 2026Peer-reviewedCC BY 4.0Full textNeuroscience Algebra & number theoryPLOS
Strategic Information Transmission over Gossip Networks
Emirhan Tekez, Melih Bastopcu, Sinan Gezici
arXiv4 Sep 2026PreprintarXiv licenceFull textGeneral mathematicsarXiv
Privacy Preserving Gossip Learning
Erkan Bayram, Mohamed-Ali Belabbas, Tamer Başar
arXiv13 Sep 2026PreprintarXiv licenceFull textOptimisation & controlarXiv
Serverless gossip training of LSTM failure detectors: A matched-protocol comparison with federated, local and centralized learning on NASA C-MAPSS
Yusuf Öztürk, Enes Göktekin, Bengisu Atlı +3
arXiv17 Sep 2026PreprintCC BY-NC-ND 4.0Full textStatisticsarXiv

Cite

BibTeX RIS

BibTeX

@misc{poda2026multiplex,
  title = {{Multiplex relational event models of self-reported and perceived gossip}},
  author = {Veronica Poda and Veronica Vinciotti and Ernst C. Wit},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.35162},
  eprint = {2610.35162},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.35162},
}

RIS

TY  - UNPB
TI  - Multiplex relational event models of self-reported and perceived gossip
AU  - Veronica Poda
AU  - Veronica Vinciotti
AU  - Ernst C. Wit
PY  - 2026
DA  - 2026/10/26
PB  - arXiv
DO  - 10.48550/arxiv.2610.35162
UR  - https://arxiv.org/abs/2610.35162
AB  - Gossiping is a complex social phenomenon that unfolds through multiple types of relations. Among other things, individuals may initiate gossiping about others as a result of believing themselves to be the target of gossip. These two processes, i.e., gossiping and gossip perception, form a dynamic multiplex network, where self-reported and perceived gossip ties coexist and can influence each other over time. We develop a multiplex relational event model to study the joint evolution of these two processes. The proposed model includes endogeneous covariates that capture the influence of events occurring within one layer, as well as events occurring on the other layer, on the rate of self-reported and perceived gossiping. We analyze survey data collected annually from 44 secondary schools in Hungary. We show how a multiplex relational event model, including linear, smooth and random effects, is able to describe the social mechanisms operating within and between self-reported and perceived gossiping and to capture how these two layers influence each other over time.
ER  -

Abstract

Related

Cite