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    Area of Science:

    • Social Network Analysis
    • Computational Social Science
    • Information Visualization

    Background:

    • Sentiment contagion, the spread of attitudes across topics, is crucial for understanding social media dynamics.
    • Existing methods struggle with the scale and complexity of social media, hindering efficient network construction and in-depth analysis.
    • Challenges include managing vast topic volumes and intricate interrelationships.

    Purpose of the Study:

    • To develop an efficient, causality-based framework for constructing and explaining large-scale sentiment contagion networks.
    • To introduce a novel visualization technique for scalable and intuitive exploration of sentiment flow over time.
    • To present CausalMap, a system for tracing contagion pathways and assessing demographic influence.

    Main Methods:

    • A causality-based framework for efficient sentiment contagion network construction and explanation.
    • A map-like visualization encoding time on the horizontal axis for clear sentiment flow representation.
    • Development of the CausalMap system for pathway tracing and influence assessment.

    Main Results:

    • The proposed framework and visualization technique enable efficient construction and exploration of sentiment contagion networks.
    • CausalMap effectively supports analysts in tracing sentiment pathways and understanding demographic influences.
    • Comprehensive evaluations, including user studies and case studies, validated the system's usability and effectiveness.

    Conclusions:

    • The causality-based framework and CausalMap visualization offer a scalable and effective solution for analyzing sentiment contagion on social media.
    • This approach enhances the understanding of topic evolution and social influence dynamics.
    • The findings have implications for social policy and the analysis of online information diffusion.