Sentence-level social network analysis pipeline for eye-tracking reading data

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description

A reusable analytic pipeline in which fixations are filtered (Tobii I-VT), sentence AOIs are classified as topic-introducing, topic-medial or topic-ending, AOI strings are converted to transition matrices, and weighted networks are built in R with igraph to compute strength, betweenness, harmonic centrality and local clustering coefficient. The authors state "The dataset and scripts are available at osf.io (https:// osf. io/ qbctd/)", supporting reuse and replication of the approach.

Accounts

• Sentence-level social network analysis pipeline for eye-tracking reading data (OSF dataset and scripts): A reusable analytic pipeline in which fixations are filtered (Tobii I-VT), sentence AOIs are classified as topic-introducing, topic-medial or topic-ending, AOI strings are converted to transition matrices, and weighted networks are built in R with igraph to compute strength, betweenness, harmonic centrality and local clustering coefficient. The authors state "The dataset and scripts are available at osf.io (https:// osf. io/ qbctd/)", supporting reuse and replication of the approach. (Catrysse et al. (2025))

Claims

• Topic-medial sentences have higher strength than topic-initial and topic-final sentences [+W]

Related Research Methods

• Microscopic network measures as indicators of sentence-level integration during reading

Key Sources

• Catrysse, L., van Daal, T., Jarodzka, H., Kaakinen, J. K., Donche, V., & Gijbels, D. (2025). How Eye Read: A Social Network Approach. Educational Psychology Review. https://doi.org/10.1007/s10648-025-10000-y

Page metadata

Field	Value
type	research-method
id	sentence-level-social-network-analysis-pipeline-for-eye-tracking-reading-data
title	Sentence-level social network analysis pipeline for eye-tracking reading data
description	A reusable analytic pipeline in which fixations are filtered (Tobii I-VT), sentence AOIs are classified as topic-introducing, topic-medial or topic-ending, AOI strings are converted to transition matrices, and weighted networks are built in R with igraph to compute strength, betweenness, harmonic ce
status	review
generated	by process:settle-candidates on 2026-10-20
trust	unverified — no one has confirmed this page's content

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