Scalability and Performance Evaluation of Machine Learning Techniques in High-Volume Social Media Data Analysis

In the modern era of digital communication, social media platforms are data factories and hence provide a new set of opportunities as well as challenges before the analysts. The objective of this paper is to evaluate the scalability and performance of various ML algorithms for social media data analysis in practice with high-volume datasets. The objective is to design ML algorithms that are scalable and achieve high accuracy when used with big datasets. In a literature survey, observed gaps in the present research advancements toward scalability and performance analysis for various ML techniques used for this intent. To make sure of diversity and size, we collected a big source from different social networks. The approach we did involve assessing a number of ML methods that are widely known like natural language processing (NLP), sentiment analysis (SA) and predictive analytics on scalability factors such as time to process and resource utilization while also considering performance metrics such as accuracy precision recall rate f1 score. The results demonstrate significant differences in the scalability of investigated approaches and their quality, such as some algorithms being more efficient or accurate at a large scale. This research adds value to the existing works in this area, by performing a detailed comparative study of ML techniques for large-scale social media data processing considering scalability vs performance tradeoffs. The findings are informative, for the research and practice community, on selection of suitable ML techniques that will be applicable to social media analytics; scalability is an important factor in this era dominated by big data. In future research, more advanced techniques and hybrids should be taken into account to enhance the scalability and performance of social media data analysis.