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Abstract:
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Technological advancement has made possible the collection of data from social media platforms at unprecedented speed and volume. Current methods for analyzing such data either lack interpretability, are computationally intense or require a rigid data regimen. In this work, we propose a flexible statistical framework for the analysis of high-resolution data arising from social media applications. We focus primarily on the posting behavior of social media users and consider functional data-based methods to extract relevant information of a user’s posting behavior and ultimately identify the type of account (malicious or genuine). We illustrate the methods numerically and on the motivating Twitter data set. The developed methods are applicable to other social media data, such as Facebook, Instagram, Reddit, or TikTok, or any form of digital interaction where user’s posting behavior is a key feature of their type.
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