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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence.

Jina Park1,2, Ick Hoon Jin3,4, Minjeong Jeon5

  • 1Department of Applied Statistics, Seoul, Republic of Korea.

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|September 23, 2023
PubMed
Summary

This study introduces a new method to measure social influence using item-level behavior data, creating an "interaction map" to visualize how social networks affect individual actions. The approach reveals nuanced influence across different behaviors and provides a comprehensive measure of social impact.

Keywords:
differential social influenceitem response modellatent space item response modellatent space modelnetwork analysispeer Network

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Area of Science:

  • Social Psychology
  • Network Analysis
  • Behavioral Science

Background:

  • Understanding social network influence on human behavior is crucial in applied research.
  • Traditional methods often rely on aggregated, scale-level behavioral data.
  • A gap exists in analyzing fine-grained, item-level behavioral responses to social influence.

Purpose of the Study:

  • To propose a novel approach for studying social influence using item-level behavioral measures.
  • To develop an 'interaction map' integrating social network structure and individual behaviors.
  • To reveal differential social influence effects across specific behaviors and measure overall social impact.

Main Methods:

  • Latent space modeling framework to integrate social network and item-level behavior data.
  • Creation of an 'interaction map' to visualize associations between respondent homophily and behaviors.
  • Simulation studies for methodological validation and real-world data application.

Main Results:

  • The 'interaction map' effectively visualizes the relationship between social network structure (homophily) and item-level behaviors.
  • Differential social influence effects were identified across various item-level behaviors.
  • The proposed method provides a robust measure of overall social influence.

Conclusions:

  • Item-level behavioral analysis offers a more nuanced understanding of social influence than scale-level data.
  • The 'interaction map' is a valuable tool for exploring complex social dynamics and influence.
  • The novel approach demonstrates practical application in understanding how social networks impact participation in activities.