Ground Zero

(excerpt from a working document by James Abello, Lev Manovich, Jianbo Gao, Katy Börner, and Tina Eliassi-Rad)

The Arrowhead Problems in Culture Analytics

  1. Metrics for the study of culture(s)
  2. Identifying, defining and measuring cultural complexities
  3. Is culture automatically the result of the evolution of groups (see Hilbert Problem #5)
  4. What are the fundamental mechanisms for cultural network formation?
  5. Find algorithms to detect the culturally meaningful topical structure of heterogeneous cultural data (see Hilbert Problem #10)
  6. Find algorithms to detect culturally meaningful phase transitions in heterogeneous cultural data (see Hilbert Problem #10)
  7. Identify invariance of offline and online culture(s) to understand their co-evolution
  8. Measure the impact of culture on health (e.g., there are different narratives/reasons why groups of people do not vaccinate their kids), social conflict, inequality, and the environment (social, env, public goods)
  9. Identify the densities and velocities of changing areas in culture(s) both online and offline
  10. Scaling algorithms to all heterogeneous cultural data
  11. Can one develop a calculus of culture?
  12. Are there axioms of culture and can one develop a mathematical treatment of these? (see Hilbert Problem #6)
  13. How do we measure the cultural impact of globalization?


Grand Challenges

Theoretical Challenges

    • Properties of cultural systems (what correlations, statistical models, laws exist?)
    • Phase transitions (e.g., perception of tattoos, viral spread)
    • How to measure, model, and promote cultural diversity

Empirical Challenge

    • Hypothesis testing of cultural assumption (e.g., acceleration of culture=perception of time is compressing as time progresses, need listing), validate humanistic approaches for understanding culture
    • Validate cultural paradoxes
    • AB testing for XXX
    • Creation of an open source software package that is accessible, module, adaptable and that allows for reproducibility.


Engineering/CS Challenges

    • Scalability (to trillions of records/PB of data)
    • Usability
    • Reproducibility
    • Long tail?

Practical/Societal Challenges

  • Improve health: Use social media data to predict and prevent episodes of depression
  • Reduce substance abuse: Contextualize people’s experiences and behaviors in overall contexts
  • Promote global peace by analyzing media reported events and identifying bifurcation points
  • Promote stability by decreasing (education, economic, and health) inequalities
  • Reduce education inequality: Teach diverse cultures
  • Privacy
  • Ethics
  • Access