Instagram Content Analysis of a Higher Education Institution using Social Media Analytics and Topic Modeling

Jum'atul Fitri, Ida Ariyani Hasanah, Setyawan Widyarto

Abstract


Social media is an important communication channel for higher education institutions to share information and build institutional identity. This study analyzes Instagram content from a selected institutional account, including collaborative posts involving other accounts. A descriptive quantitative approach was applied to 100 valid posts collected through web scraping. The analysis combined Exploratory Data Analysis (EDA) to describe content formats, posting activity, likes, and comments with Latent Dirichlet Allocation (LDA) to identify recurring themes in captions. The results showed that image posts were the most common format, followed by carousel posts and videos. LDA identified three main themes: student achievement and development, religious values and character building, and institutional collaboration and community engagement. Religious values and character building accounted for the largest proportion of posts and had the highest average numbers of likes and comments among the identified topics. Content specifically focused on student admission was less prominent in the identified themes. These findings provide descriptive insights for future content planning; however, they do not establish causal relationships between content characteristics, engagement, or admission outcomes.

Keywords


higher education; instagram analytics; latent dirichlet allocation; social media analytics; topic modeling

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DOI: https://doi.org/10.32520/stmsi.v15i10.6946

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