Identifying exogenous and endogenous activity in social media

Abstract

The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions of exogenous, e.g. news media, and endogenous, e.g. information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.

arXiv comment5 figures

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Cited by in corpus (6)

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• Unraveling the Origin of Social Bursts in Collective Attention
• Estimating Community Feedback Effect on Topic Choice in Social Media with Predictive Modeling
• 2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models
• The statistical physics of discovering exogenous and endogenous factors in a chain of events
• Asymmetric excitation of left- and right-tail extreme events probed using a Hawkes model: application to financial returns