High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
<|reference_start|>High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media: Motivated by the empirical observation of power-law distributions in the credits (e.g., "likes") of viral social media posts, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we present a regularized estimator, establish its consistency, and derive its convergence rate. Second, we introduce a debiasing technique for the regularized estimator to facilitate inference and prove its asymptotic normality. Third, we extend our approach to handle large-scale online streaming data using stochastic gradient descent. Simulation studies corroborate our theoretical findings. We apply these methods to the text analysis of viral posts on X (formerly Twitter) related to LGBTQ+ topics.<|reference_end|>
@article{sasaki2024high-dimensional,
    title={High-Dimensional Tail Index Regression: with An Application to Text
  Analyses of Viral Posts in Social Media},
    author={Yuya Sasaki, Jing Tao, Yulong Wang},
    journal={arXiv preprint arXiv:2403.01318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01318},
    primaryClass={stat.ML cs.LG econ.EM}
}