Summary of High-dimensional Tail Index Regression: with An Application to Text Analyses Of Viral Posts in Social Media, by Yuya Sasaki et al.
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
by Yuya Sasaki, Jing Tao, Yulong Wang
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel high-dimensional tail index regression model is introduced in this paper, motivated by the empirical observation of power-law distributions in social media post credits. The model is designed to estimate and infer parameters for understanding the underlying dynamics of viral content. A regularized estimator is presented, along with proofs of its consistency, convergence rate, and asymptotic normality. Additionally, a debiasing technique is proposed to facilitate inference. The approach is extended to handle large-scale online streaming data using stochastic gradient descent. Simulation studies validate the theoretical findings. Finally, the methods are applied to the text analysis of viral posts on X (formerly Twitter) related to LGBTQ+ topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how social media posts go viral and why some things become super popular. The researchers created a new way to analyze data from social media platforms like Twitter. They found that when people share interesting or funny content, it spreads quickly because of the way we interact with each other online. The team developed a special tool to help understand this process better. They tested their tool on real data and found it works well. This is important for understanding how ideas spread and why certain topics become popular. |
Keywords
* Artificial intelligence * Inference * Regression * Stochastic gradient descent
