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Dual-induced multifractality in online viewing activity
Yu-Hao Qin1, Zhi-Dan Zhao1, Shi-Min Cai1
1Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
Chaos (Woodbury, N.Y.)
|February 3, 2018
Summary
Human online viewing activity exhibits long-term correlations and multifractality, extending fractal properties into cyberspace. This behavior stems from event timing patterns and viewing activity correlations.
Area of Science:
- Complex systems
- Network science
- Human dynamics
Background:
- Human activity, including online viewing, shows long-term correlations and fat-tailed distributions.
- Fractality has been observed in human activity, but its origins in cyberspace remain unclear.
Purpose of the Study:
- To analyze the fractality and its origins in online viewing activity from Movielens and Netflix.
- To investigate long-term correlations at individual and communal levels in online viewing patterns.
Main Methods:
- Detrended fluctuation analysis (DFA)
- Multifractal detrended fluctuation analysis (MF-DFA)
- Analysis of time series data from Movielens and Netflix viewing activity
Main Results:
- Confirmed long-term correlations in online viewing at both individual and communal levels.
- Demonstrated that individual correlation extent depends on activity level.
- Identified multifractality arising from fat-tailed inter-event times and long-term correlations.
Conclusions:
- Multifractality in online viewing is driven by fat-tailed inter-event times and long-term correlations.
- Fractal properties of human activity now encompass cyberspace, not just physical space.
- Findings expand the understanding of complex systems dynamics in digital environments.
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