Clustering Users as Trigger of Twitter Trending Topic Using Pandas Python and SQL Query

This study aims to analyze trending topic data on Twitter using Python and SQL filters to understand which users trigger and have the most influence on the formation of trending topics. Scalping methods using Node.js, Python, and RapidMiner Studio were used to retrieve data from Twitter with filters based on hashtags and dates. The filtered data was then analyzed using SQL to identify the first users who triggered the trending topics and the users who had the most impact. The results and discussion of this study demonstrate the successful use of scalping methods to retrieve trending topic data on Twitter. The data analysis revealed the users who triggered the trending topics and the users who had the most influence on their formation. The results were visualized through graphs to illustrate trends in tweet volume over time. The conclusion of this study is the successful analysis of trending topic data on Twitter using scalping methods. Recommendations for future research include the development of more automated systems, integration with other social media platforms such as Facebook or Instagram, and further updates to improve the efficiency and relevance of trending topic data analysis on Twitter. This study provides significant benefits in understanding the phenomenon of trending topics on Twitter and offers insights into the users who influence their occurrence.