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Related Experiment Video

Updated: Aug 4, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Quantifying External Information in Social Network Analysis: An Application to Comparative Mythology.

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    This study introduces a novel social network analysis method that integrates domain-specific knowledge, improving relationship insights. The semantic value and affinity measures offer more meaningful comparisons across diverse networks, from mythology to real-world data.

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    Area of Science:

    • Network Science
    • Computational Social Science
    • Information Science

    Background:

    • Classical social network analysis (SNA) excels at revealing structural relationships but often overlooks domain-specific knowledge.
    • Integrating external information into SNA is crucial for a comprehensive understanding of network dynamics and information propagation.

    Purpose of the Study:

    • To extend classical SNA by incorporating domain-specific knowledge from the network's source.
    • To introduce novel metrics: semantic value (centrality measure) and semantic affinity (relationship function).
    • To develop a heuristic algorithm for computing semantic affinity based on the shortest capacity problem.

    Main Methods:

    • Developed an extension of social network analysis to incorporate external, domain-specific information.
    • Proposed a new centrality measure (semantic value) and a new fuzzy-like affinity function (semantic affinity).
    • Implemented a heuristic algorithm based on the shortest capacity problem for computing semantic affinity.

    Main Results:

    • Applied the novel methods to analyze Greek, Celtic, and Nordic mythologies, comparing gods and heroes.
    • Tested the approach on the Reuters terror news network and a COVID-19 Twitter network.
    • Demonstrated that the proposed method yields more meaningful results than existing centrality measures and embedding approaches across all case studies.

    Conclusions:

    • The novel semantic approach enhances social network analysis by integrating domain knowledge, leading to richer insights.
    • The semantic value and semantic affinity provide a more nuanced understanding of relationships within networks.
    • This extended SNA framework proves effective and versatile, offering superior analytical capabilities for diverse network types.