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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Multiscale Entropy Analysis of Page Views: A Case Study of Wikipedia
Chao Xu1, Chen Xu1, Wenjing Tian1
1School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430200, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
Human website search complexity was analyzed using Wikipedia page views and sample entropy. Results show distinct temporal variations and rankings for topics like education and medicine, validated by statistical tests.
Area of Science:
- Complex systems analysis
- Information science
- Computational social science
Background:
- Understanding human information-seeking behavior is crucial.
- Wikipedia page view data offers insights into public interest and search complexity.
- Entropy measures complexity, but its application to time-series website data requires specific methods.
Purpose of the Study:
- To analyze the complexity of human website searching activities.
- To investigate temporal variations in topic popularity using Wikipedia page views.
- To assess the feasibility of the short-time series multiscale entropy (sMSE) algorithm for this analysis.
Main Methods:
- Collected Wikipedia page view data for education, economy/finance, medicine, and nature/environment from 2016-2018.
- Estimated sample entropies using the short-time series multiscale entropy (sMSE) algorithm.
- Performed non-parametric statistical analysis (Wilcoxon signed-rank test, Mann-Whitney U-test) with 95% confidence intervals.
Main Results:
- Sample entropies of the selected topics exhibited different temporal variations.
- The temporal characteristics and tendencies of sample entropies were revealed and could be quantitatively ranked.
- Statistical validation confirmed the temporal variations of sample entropies.
Conclusions:
- The sMSE algorithm is feasible for analyzing temporal complexity variations in website search data.
- Regular variations in sample entropies across different topics require careful interpretation.
- Further research is needed to explore potential explanations for observed patterns.
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