[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121473-en":3,"doc-seo-121473-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121473,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Utilizing logistic regression in machine learning for categorizing social media advertisement - Article","This paper investigates how logistic regression in machine learning can distinguish categories of social media advertisements. Because logistic regression is designed for classification tasks with categorical target outputs, it is used with a dedicated social media advertisement dataset. The study emphasizes evaluating the model using performance metrics to quantify how effectively it separates advertisement types. Findings indicate that logistic regression is suitable for categorizing social media advertisements and can predict categorical results based on advertisement characteristics, supporting more reliable targeting.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 37, No. 3, March 2025, pp. 1954∼ 1963  \nISSN: 2502-4752, DOI: 10.11591/ijeecs.v37.i3.pp1954-1963 ❒ 1954  \n\n| Utilizing logistic regression in machine learning for categorizing social media advertisement\u003Cbr>Hari Gonaygunta, Geeta Sandeep Nadella, Karthik Meduri\u003Cbr>Department of Information Technology, University of the Cumberlands, Williamsburg, USA |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Apr 13, 2024 Revised Sep 28, 2024 Accepted Oct 7, 2024\u003Cbr>Keywords:\u003Cbr>Classification model Explanatory variables Logistic regression Performance metrics Predictive modeling\u003Cbr>Social media advertisements | ABSTRACT\u003Cbr>The purpose of this paper is to investigate the use of logistic regression in machine learning to distinguish the types of social media advertisements. Since the logistic regression algorithm is designed to classify data with a target variable that has categorical results, it is the one selected. As a result, this research intends to measure the efficiency of logistic regression for the classification of social media advertisements. This research centers on the social media advertisements dataset and employs logistic regression for classification purposes. The model is evaluated against performance metrics to measure the extent to which it can categorize social media advertisements. As a result, the findings of this study show that logistic regression is fit for classifying social media advertisements. Logistic regression is important for machine learning when it comes to classifying social media advertisements because it supports categorizing advertisements according to their characteristics and precisely predicts the categorical results. |  |\n| This is an open access article under the CC BY-SA license. |  |  |\n| Corresponding Author: |  |  |\n| Geeta Sandeep Nadella\u003Cbr>Department of Information Technology, University of the Cumberlands Williamsburg, Kentucky, USA\u003Cbr>Email: [geeta.s.nadella@ieee.org](geeta.s.nadella@ieee.org) |  |  |\n\n1. INTRODUCTION  \nToday, social media has risen to be one of the most powerful tools for marketing goods and services. As millions of people engage with social media every day and a multitude of ads are published, the correct classification of these ads is essential to improve targeting efficiency and maximize the returns on advertisers’investments [1] . Nevertheless, the categorization of social media advertisements is often quite tricky, largely because of the nature and wide range of advertisements. In response to this challenge, machine learning techniques are progressively utilized to organize the content and increase the dependability of the method. One of the usual machine-learning algorithms, logistic regression, has the potential to classify social media advertisements [2] .  \nUsing logistic regression in machine learning to classify social media advertisements is a trustworthy and clear method. The methods of machine learning in many different disciplines, including objective prediction models, are much like logistic regression [3] . Logistic regression is being employed on social media to investigate the link between social media use and adolescent sleep quality and physical activity [4] . A machinelearning method called logistic regression has been put forth for display advertising to deal with the features of this industry [5] . Logistic regression has been combined with other methods to predict customer advertisement clicks; this proves that it can be used to estimate the click-through rates of new advertisements [6] . Logistic regression has found use in modeling customer engagement behavior related to social media advertising, prov-  \ning its adequacy in examining the factors that affect and the outcomes of user engagement [7] . Also, logistic regression has been applied in predicting advertising click-through rates, which illustrates the practical use of the model in addressi","cbCaibe7itxaqJOs","https://ap.wps.com/l/cbCaibe7itxaqJOs","pdf",2182831,1,10,"English","en",105,"# Introduction\n## Background on social media advertising classification\n## Role of logistic regression in ML-based categorization","[{\"question\":\"What is the main goal of the paper?\",\"answer\":\"The paper aims to evaluate logistic regression for distinguishing categories of social media advertisements using a machine learning classification setup.\"},{\"question\":\"Why was logistic regression selected for this task?\",\"answer\":\"Logistic regression is suitable for classification problems where the target results are categorical, matching the goal of labeling advertisement types.\"},{\"question\":\"How is the model assessed in the study?\",\"answer\":\"The research evaluates logistic regression against performance metrics to measure how well it categorizes social media advertisements.\"}]","Utilizing logistic regression in machine learning for categorizing social media advertisement - 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