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All Journal Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Muhammad Ravlyansyah
Universitas Muhammadiyah Jakarta
Humanities Environmental Science Social Sciences

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Behind The Trend: Analisis Tren dan Sentimen Publik Terhadap Brainrot Content di Media Sosial Menggunakan Python dan Google Colab Indri Surya Ningsih; Hafizh Umar Haq; Muhammad Ravlyansyah; Lantip Nurrohman; Muhammad Naufal Razani; Syamil Ghufron Rabbani; Sitti Nurbaya Ambo; Jumail Jumail; Nurvelly Rosanti; Yana Adharani; Rully Mujiastuti; Popy Meilina
KENDURI : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 2 (2026): May-August
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/kenduri.v6i2.2772

Abstract

Brainrot content, short-form social media content characterized by low-quality yet highly addictive information, has grown rapidly among younger audiences and is associated with declining attention span and mental well-being. This community service activity aims to improve students' digital literacy and analytical skills in reading public trends and sentiment toward brainrot content through a webinar and workshop titled "Behind The Trend". The activity was held online via Zoom Meeting on 26 June 2026, featuring two speakers from the Dicoding community (DBS Foundation and PIJAK). The method consisted of a conceptual webinar followed by a hands-on workshop covering YouTube comment web scraping, data cleaning, text preprocessing, N-Gram and WordCloud generation, and lexicon-based sentiment analysis using Python on Google Colab. Evaluation was conducted through Google Form-based pre-test and post-test. The pre-test involving 18 respondents recorded an average correctness of 81.1%, while the post-test involving 23 respondents showed an increase with an average score of 95.2 out of 100. The activity was attended by 53 registered participants from various universities. These results indicate that a viral-trend case-study training approach effectively improves participants' understanding of data scraping, text mining, and sentiment analysis concepts.
Co-Authors Adharani, Yana Hafizh Umar Haq Indri Surya Ningsih Jumail, Jumail Lantip Nurrohman Muhammad Naufal Razani Nurvelly Rosanti, Nurvelly Popy Meilina Rully Mujiastuti Sitti Nurbaya Ambo Syamil Ghufron Rabbani