Reconstructing social sensitivity from evolution of content volume in Twitter

Area of Science:

• Computational Social Science
• Mathematical Modeling
• Sociology

Background:

• Public interest in societal events is influenced by media coverage and social interactions.
• Understanding the dynamics of public response is crucial for analyzing social phenomena.

Purpose of the Study:

• To develop and test a mathematical model for public interest dynamics.
• To investigate the relationship between media coverage, social interactions, and public response.
• To infer social sensibility and its correlation with human mobility.

Main Methods:

• Constructed a simple mathematical model for public interest dynamics.
• Utilized Twitter (tweets and retweets) as a proxy for public interest.
• Employed news volume as a proxy for media coverage.
• Tested the model on US violence events data from 2020.

Main Results:

• The mathematical model demonstrated a successful fit with the collected data.
• Inferred a measure of social sensibility from the model.
• Found a correlation between social sensibility and human mobility data.

Conclusions:

• The study identifies key mechanisms regulating social responses and public mobilization.
• The model provides insights into the interplay of media, social interactions, and public interest.
• Findings suggest a quantifiable link between social sensibility and real-world human behavior.