USING RELATIONAL LEARNING IN EXPLORING THE EFFECTIVENESS OF USING HASHTAGS IN FUTURE TOPICS AND USER RELATIONS IN X

Abstract

This research has a research object, namely relationallearning witha mathematical modeling approach from graph queries for exploring future topics and user relationships. The problem in this research is the large and varied number of tweets that are produced every day, where each use of hashtags always increases, which has an impact on the accumulation of data that needs to be processed to obtain information because users on social media X can interact to influence trends so as to solve the problem. This requires the application of relational learning by utilizing graph query mathematical models. The results obtained from this research are in the form of a model that can produce predictions of future topics and see user relationships based on interactions on social media with relationships between entities at interconnected nodes. In applying relational learning with mathematical models utilizing graph queries there will be a process of examining the relationships between entities, content and communication interactions in accordance with the definitions and theorems that have been described to observe each node. In relational learning, there will be each node according to the entity used, then the mathematical model with graph queries will connect all the entities to form a graph that can be used as a model for predicting future topics and relationships between users. This research is research with a level of novelty in applying graphical queries to mathematical models to predict future topics and applying relational learning to user relationships so that it can add information related to future communication. Graph queries aim to model a node between relations in the data so that it can represent a relationship between variables