Trend Detection and Popular Topics on Social-Media Using a clustering algorithm to find patterns and topics that are going viral on the Instagram platform

Jamaq, Evan
Hasan, Abdul Aziz
Kurniawan, M Rizal
Salisu, Imam Auwal

JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Vol. 2 No. 2 (2025): June

This study aims to cluster Instagram posts based on hashtags and the number of likes using the K-Means Clustering algorithm. The data used is data that represents various popular topics on social media, such as travel, culinary, fashion, and local coffee. The analysis process involves data preprocessing, clustering algorithm implementation, and result evaluation to identify patterns and trends among users. The results successfully grouped posts into three main clusters, namely clusters with low engagement, clusters related to local food and coffee, and clusters with high engagement on travel and fashion topics. This clustering provides useful insights for marketers, content creators, and researchers in understanding social media user behavior and designing more effective marketing strategies. This research confirms the importance of data analysis as a tool to uncover hidden patterns and support data-driven decision-making.