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Clustering Social Media Users Using Categorical-Valued Functional Data Analysis
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DOI：10.1080/01621459.2026.2672226.png)
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Abstract
En 中文
Social media provides more insight into consumer behavior than companies have ever had, and firms can interact with consumers on social media to increase their brand loyalty and address concerns they might have. However, it is critical for companies to evaluate whether the consumers they interact with have the potential to positively promote the firm. This can be challenging, especially when limited information is available about social media users. Our work proposes a flexible methodology to cluster many Twitter users based on the similarity in their posting behavior to solve this problem. We provide a framework that views users’ high-frequency postings during a specified timeframe as densely-observed categorical functional data, and propose to cluster them using latent user-specific characteristics. This leads to an interpretable and computationally-efficient algorithm and enables us to gain insights into the posting behavior of social media users. While our methods are inspired by a Twitter application they can be applied to understand posting behavior across various social media platforms. Finite-sample properties of the methods are investigated through simulations. This method is implemented in the function catfdcluster() in the R package catfda. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keywords:
Categorical functional data analysis
Clustering
Multivariate latent process
Twitter
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