Dual-induced multifractality in online viewing activity

TL;DR

This study reveals that online viewing activity exhibits multifractality caused by both fat-tailed inter-event time distributions and long-term correlations, expanding understanding of human activity patterns into cyberspace.

Contribution

It is the first to identify multifractality in online viewing activity resulting from combined effects of inter-event time distribution and correlations.

Findings

Long-term correlations exist at individual and communal levels.

Multifractality arises from both fat-tailed distributions and correlations.

The extent of correlation depends on activity level.

Abstract

Although recent studies have found that the long-term correlations relating to the fat-tailed distribution of inter-event times exist in human activity, and that these correlations indicate the presence of fractality, the property of fractality and its origin have not been analyzed. We use both DFA and MFDFA to analyze the time series in online viewing activity separating from Movielens and Netflix. We find long-term correlations at both the individual and communal level, and that the extent of correlation at the individual level is determined by the activity level. These long-term correlations also indicate that there is fractality in the pattern of online viewing. And, we firstly find a multifractality that results from the combined effect of the fat-tailed distribution of inter-event times (i.e., the times between successive viewing actions of individual) and the long-term correlations in online viewing activity and verify this finding using three synthesized series. Therefore, it can be concluded that the multifractality in online viewing activity is caused by both the fat-tailed distribution of inter-event times and the long-term correlations, and that this enlarges the generic property of human activity to include not just physical space, but also cyberspace.