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    Area of Science:

    • Computational Social Science
    • Media Studies
    • Data Visualization

    Background:

    • Media framing influences public interpretation of events.
    • Shifts in framing can alter discourse and viewpoints.
    • Identifying causes of framing shifts is challenging.

    Purpose of the Study:

    • To present a visual analytics framework for event cueing using media data.
    • To enhance hypothesis generation by linking secondary datasets.
    • To explore relationships between framing and real-world events.

    Main Methods:

    • Developed a visual analytics framework for event cueing.
    • Applied time series intervention models to detect framing changes.
    • Utilized entity extraction and sentiment analysis for data exploration.
    • Employed two intervention analysis models.

    Main Results:

    • The framework successfully cues analysts to explore potential event triggers for discourse shifts.
    • Demonstrated the framework's utility in a case study on climate change and African conflicts.
    • Identified statistically significant differences in framing before and after specific dates.

    Conclusions:

    • Visual analytics combined with time series intervention models can reveal significant changes in media framing.
    • This approach aids in understanding the impact of events on public discourse.
    • The framework offers a novel method for media discourse analysis and hypothesis generation.