A New Bivariate Point Process Model with Application to Social Media User Content Generation
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In this paper, we propose a new bivariate point process model to study the activity patterns of social media users. The proposed model not only is flexible to accommodate but also can provide meaningful insight into the complex behaviors of modern social media users. A composite likelihood approach and a composite EM estimation procedure are developed to overcome the challenges that arise in parameter estimation. Furthermore, we show consistency and asymptotic normality of the resulting estimator. We apply our proposed method to Donald Trump's Twitter data and study if and how his tweeting behavior evolved before, during and after the presidential campaign. Moreover, we apply our method to a large-scale social media data and find interesting subgroups of users with distinct behaviors. Additionally, we discuss the effect of social ties on a user's online content generating behavior.
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