Bibliometric Analysis of Big Data Visualization and Visual Analytics in Social Media Analysis: Techniques, Tools, and Trends

Background of Study: Big data visualization and visual analytics are essential in social media analysis because they help process large and complex data into more understandable information. With this technique, we can identify patterns, trends, and relationships in social media data, such as user interactions and social influences, that are difficult to analyze without visualization tools. This allows for faster, more accurate, and in-depth analysis, and helps with data-driven decision-making in a variety of areas. Aims and Scope of Paper:  The purpose of this paper is to review the literature related to big data visualization and visual analytics in the context of social media, using bibliometric analysis methods to identify current trends, techniques used, and important tools. Methods: This study used a bibliometric design to analyze the literature in the Scopus database with three main keyword combinations: "Big Data Visualization" AND "Social Media Analysis", "Visual Analytics" AND "Bibliometric Analysis", as well as "Data Visualization" AND "Social Media Analytics". The literature analyzed consisted of journals published between 2015 and 2025.  After data collection, filtering is carried out using OpenRefine to eliminate bias and duplication, ensure the accuracy and validity of the data, resulting in objective insights into trends in data visualization and social media analytics. Results: A summary of key findings on the most widely used techniques and tools in big data visualization on social media. Conclusion: Closing on the contribution of bibliometric analysis in understanding the development of big data visualization in social media research.