Evidence map›Paper›PMID 42622040›Full record

ArticleConnections (Toronto, Ont.)2024

Contagion and Interpersonal Influence: Distinguishing Mechanisms of Behavior Change Using Social Network Theory.

Thomas W Valente

Abstract read
In one paragraph

Article in Connections (Toronto, Ont.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Thomas W ValenteDepartment of Population & Public Health Sciences, Keck School of Medicine, University of Southern California.

Funding

Using Social Network Analysis to Understand Peer Influences on ENDS UseR01DA051843 · NIDA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VALENTE, THOMAS W · 2020 to 2024
$2.8M
NIDA NIH HHS R01 DA051843
6 · The paper itself

Abstract

The present paper articulates the many ways social network researchers conceptualize and operationalize network influences on people's behaviors. Four categories of network influences are described: (1) personal network, (2) positional, (3) network-level, and (4) individual network-level interactions. Personal network effects are based on data from the individual's direct and indirect contacts. Positional effects are derived from the individual's position in the network such as being central or peripheral. Network-level effects are measured using network-level indicators such as centralization or clustering. Interaction effects occur when there is consideration of both the individual and network level measures such as understanding the influence of being in a central position in a centralized or decentralized network. These various network effects are contrasted with contagion, which is the most frequently used mechanism for diffusion. One conclusion drawn from this review is that when we invoke contagion explanations, we give agency to the product, whereas when we invoke interpersonal influence explanations, we give agency to people and social systems.

Indexed as

ContagionDiffusion of InnovationsNetwork EffectsSocial Influence

Identifiers

PMID42622040
PMCPMC13488863

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.