Local Variation of Collective Attention in Hashtag Spike Trains

TL;DR

This paper introduces a novel method inspired by neuroscience to analyze the temporal patterns of hashtag activity on Twitter, revealing insights into collective attention during social events.

Contribution

It adapts the local variation measure from neuroscience to quantify nonlinear features in hashtag spike trains, providing a new tool for social media analysis.

Findings

Successfully characterizes burstiness and regularity in hashtag activity

Reveals temporal dynamics of collective attention during social events

Demonstrates applicability to real-world Twitter data

Abstract

In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.