Hyperevent network modelling of partially observed gossip data

TL;DR

This paper introduces a relational hyperevent model to analyze complex, dynamic gossip networks in schools, accounting for partial and interval-censored data, revealing social and temporal drivers of gossip.

Contribution

It develops a novel hyperevent modeling approach for partially observed, dynamic social interactions, extending inference methods to interval-censored data.

Findings

Identifies social factors influencing gossip dynamics

Reveals complex temporal patterns in gossip spread

Demonstrates model effectiveness on school data

Abstract

Gossiping is a widespread social phenomenon that shapes relationships and information flow in communities. From a network theoretic point of view, gossiping can be seen as a higher-order interaction, as it involves at least two persons talking about a non-present third. The mechanism of gossiping is complex: it is most likely dynamic, as its intensity changes over time, and possibly viral, if a gossiping event induces future gossiping, such as a repetition or retaliation. We define covariates of interest for these effects and propose a relational hyperevent model to study and quantify these complex dynamics. We consider survey data collected yearly from 44 secondary schools in Hungary. No information is available about the exact timing of the events nor about the aggregate number of events within the yearly time interval. What is measured is whether at least one gossiping event has occurred in a given time interval. We extend inference for relational hyperevent models to the case of rightcensored interval-time data and show how flexible and efficient generalized additive models can be used for estimation of effects of interest. Our analysis on the school data illustrates how a model that accounts for linear, smooth and random effects can identify the social drivers of gossiping, while revealing complex temporal dynamics.