Skip to content

Word-recurrence graph analysis of oral narratives

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article represents transcribed oral narratives as directed graphs in which "each node represents a word, and each directed edge represents the temporal order of consecutive words." Three connectedness attributes are computed: repeated edges (RE), largest connected component (LCC), and largest strongly connected component (LSC), using a 30-word moving window to control verbosity. The graphs "are not based on meaning but on recurrence patterns," which the authors argue lets them "reveal patterns of connectedness from the earliest stages of language acquisition because they do not rely on meaning or linguistic rules." Developmentally, RE decreases while LCC and LSC increase as children advance in schooling.

Accounts

  • Word-recurrence graph analysis of oral narratives as a measure of speech connectedness: The article represents transcribed oral narratives as directed graphs in which "each node represents a word, and each directed edge represents the temporal order of consecutive words." Three connectedness attributes are computed: repeated edges (RE), largest connected component (LCC), and largest strongly connected component (LSC), using a 30-word moving window to control verbosity. The graphs "are not based on meaning but on recurrence patterns," which the authors argue lets them "reveal patterns of connectedness from the earliest stages of language acquisition because they do not rely on meaning or linguistic rules." Developmentally, RE decreases while LCC and LSC increase as children advance in schooling. (Bárbara Malcorra et al. (2024))

Claims

-

Key Sources

  • Bárbara Malcorra, Marina Ribeiro, Luísa Jensen, Giovana Gomes, Tamara Meletti & Natália Bezerra Mota. (2024). Speech connectedness predicts reading performance three months in advance: a longitudinal experiment. npj Science of Learning. https://doi.org/10.1038/s41539-024-00248-4
Page metadata
Field Value
type research-method
id word-recurrence-graph-analysis-of-oral-narratives
title Word-recurrence graph analysis of oral narratives
description The article represents transcribed oral narratives as directed graphs in which "each node represents a word, and each directed edge represents the temporal order of consecutive words." Three connectedness attributes are computed: repeated edges (RE), largest connected component (LCC), and largest st
status review
generated by process:settle-candidates on 2026-10-27
trust unverified — no one has confirmed this page's content

Frontmatter is the page's machine-readable record — see the Schema & Guide.