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Analyzing online search trends can predict civil unrest. Google Trends data, using specific keywords, effectively indicates periods of public disturbance without compromising privacy.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Digital Footprint Analysis

Background:

  • Inferring real-world human behavior from online digital footprints is of significant interest.
  • Collective online activity may signal population mood shifts on specific topics.
  • Civil unrest, characterized by spontaneous public gatherings, presents a challenge for monitoring.

Purpose of the Study:

  • To demonstrate a privacy-preserving method for detecting indicators of civil unrest.
  • To explore the utility of aggregate online data, specifically Google Trends, for this purpose.
  • To validate this approach in Latin American countries during a period of civil unrest.

Main Methods:

  • Utilized Google Trends data for keyword search volume and momentum analysis.
  • Focused on aggregate-level data to ensure privacy.
  • Selected keyword pairs tailored to the specific cultural context of Latin America.

Main Results:

  • Found that the combination of search volume and momentum for specific keyword pairs from Google Trends served as a good indicator of civil unrest.
  • The study successfully identified periods of civil unrest across Latin America (2011-2014).
  • This approach offers a low-level, aggregate indicator of societal concerns.

Conclusions:

  • Aggregate Google Trends data, using culturally relevant keywords, can effectively indicate civil unrest.
  • This method provides a privacy-conscious alternative to analyzing individual online communications.
  • The findings support further research into more geographically granular indicators, such as urban-level geo-located searches.