Damping effect in innovation processes: case studies from Twitter

TL;DR

This paper investigates the damping effect in innovation processes, particularly in Twitter data, explaining deviations from pure power law behaviors and improving models of novelty emergence and diffusion.

Contribution

It introduces a damping mechanism in models of innovation, enhancing the fit to empirical data and explaining deviations from expected power law distributions.

Findings

The damping model fits Twitter data well for low and high frequency elements.

The model explains deviations from pure power law behaviors in empirical data.

Enhanced understanding of novelty diffusion and frequency distributions.

Abstract

Understanding the innovation process, that is the underlying mechanisms through which novelties emerge, diffuse and trigger further novelties is undoubtedly of fundamental importance in many areas (biology, linguistics, social science and others). The models introduced so far satisfy the Heaps' law, regarding the rate at which novelties appear, and the Zipf's law, that states a power law behavior for the frequency distribution of the elements. However, there are empirical cases far from showing a pure power law behavior and such a deviation is present for elements with high frequencies. We explain this phenomenon by means of a suitable "damping" effect in the probability of a repetition of an old element. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on some Twitter data sets and demonstrated great performances with respect to Heaps' law and, above all, with respect to the fitting of the frequency-rank plots for low and high frequencies.