Emergent user behavior on Twitter modelled by a stochastic differential equation

Abstract

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

References in corpus (8)

• Twitter mood predicts the stock market
• The origin of bursts and heavy tails in human dynamics
• Powerlaw: a Python package for analysis of heavy-tailed distributions
• Analysis of data sets of stochastic systems
• Emotional persistence in online chatting communities
• 1/f noise from nonlinear stochastic differential equations
• Excitable human dynamics driven by extrinsic events in massive communities
• Spreading dynamics on networks: the role of burstiness, topology and non-stationarity

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• Memory Remains: Understanding Collective Memory in the Digital Age
• Local Variation of Collective Attention in Hashtag Spike Trains