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Information flow reveals prediction limits in online social activity
James P Bagrow1,2, Xipei Liu3,4, Lewis Mitchell5,6,7
1Department of Mathematics & Statistics, University of Vermont, Burlington, VT, USA. james.bagrow@uvm.edu.
Nature Human Behaviour
|April 5, 2019
Summary
Predicting user behavior on social networks is possible using only their contacts' data. An individual's online activities can be accurately predicted using just 8-9 social ties, highlighting significant privacy concerns.
Area of Science:
- Social Network Analysis
- Information Theory
- Computational Social Science
Background:
- Online social networks generate vast user behavioral data.
- Predicting individual activities from social ties has remained a challenge.
Discussion:
- Information theoretic tools estimated predictive information in Twitter user writings.
- An upper bound for predictive information was established for any machine learning method.
Key Insights:
- 95% of potential predictive accuracy is achievable using only an individual's social ties.
- As few as 8-9 contacts can provide predictability comparable to the individual's own data.
- Distinct temporal and social effects in information flow along social ties were identified.
Outlook:
- Results have significant privacy implications, enabling individual profiling from social ties alone.
- Understanding information flow dynamics can advance online activity studies.
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