# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
###### Abstract
Grounded on this premise--that based on a target dimensionality, high-quality features can be extracted to a new subset--it is possible to combine this method with LDA roots that aim to maximize the distance between cluster centers of different classes, while also minimizing the dispersion of instances within the same class [11]. By adopting the aforementioned approach as the initial phase of a two-stage process, one can conceptualize a second step aimed at converting the subset of high-quality features into a condensed final space, contingent on the number of cluster centers. This involves addressing a multiobjective optimization problem, leading to a linear transformation that can be implemented on the subset obtained in the first stage. Inspired by this concept, our paper introduces the two-stage dimensionality reduction (TSDR) approach, a novel method designed for data classification. TSDR not only functions as an independent classifier with its built-in discriminator, but also serves as a standalone dimensionality reduction method for subsequent classifiers. Validation against benchmark datasets is part of the method's rigor; however, its primary objective lies in effectively predicting and classifying data from social media datasets. These datasets are affected by the aforementioned challenges, such as the curse of dimensionality, class masking, and imbalanced class distribution. In addition, they are also in the spotlight of a notably ongoing social transformation. In recent years, there have been remarkable advancements in data technologies, overcoming hardware and software limitations. The storage and analysis of massive datasets are now feasible. Simultaneously, in the era of artificial intelligence (AI), companies are heavily investing in solutions to enhance their understanding of people and their behavior [12]. Social networks, serving as vast repositories of user data, offer insights into preferences, affinities, and various aspects discernible to those who genuinely understand users. This raises concerns about privacy, sparking important debates regarding companies' efforts to safeguard user information [13]. Major corporations like Meta (Menlo Park, CA, USA,) responsible for platforms such as Facebook and Instagram, are implementing measures to protect and enhance customer privacy. Access to data is consistently restricted, with diminishing allowances for authorized developers. This limitation stems not only from the pressure to uphold user privacy, but also because user information is a valuable asset for these companies [14]. Despite such efforts, public profiles on social networks remain vulnerable to third-party scanning without users' consent. In many cases, access to APIs or network credentials is unnecessary. Collecting online data from social media and other platforms in the form of unstructured text is known as site scraping, web harvesting, and web data extraction [15]. By exploiting this vulnerability, one can glean various aspects of a specific user via mechanisms that scrape internet pages for raw data. By processing datasets created through these mechanisms, machine learning methods can be applied to predict patterns and metrics such as post engagement. In other words, it is feasible to predict how many interactions a post from a specific user might gain by considering their follower data. This paper makes a unique contribution by employing the TSDR method to classify social media data and explores the social implications, particularly concerning user privacy. The remainder of this paper is organized as follows. First, Section 2 outlines important concepts and background information on referenced methods. Section 3 provides the details of our proposed algorithm. Next, Section 4 reports and discusses the results of comparisons between multiple classification methods on standard benchmark classification problems. Finally, Section 5 concludes the paper and discusses potential future research directions.