Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
---|---|---|
Article Number | 04046 | |
Number of page(s) | 7 | |
Section | Computer Science | |
DOI | https://doi.org/10.1051/e3sconf/202339904046 | |
Published online | 12 July 2023 |
Machine Learning for Predictive Analytics in Social Media Data
1 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Information Technology,Faculty of Computing and Information Technology,King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
4 College of technical engineering, The Islamic university, Najaf, Iraq
5 Tashkent State Pedagogical University, Tashkent, Uzbekistan
6 National University Of Uzbekistan
* Correspondingauthor: malasafi@kau.edu.sa
wmalghamdi@kau.edu.sa
sathyanaveena_mba@psvpec.in
ahmedalkhayyat85@iunajaf.edu.iq
tolib.77777@mail.ru
Ibroximovsarvar0@Gmail.Com
Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.