Machine Learning in Social Media Account Analytics

Author(s)	Dr. Deepali Makarand Bongulwar, Mr. Aryan Raina
Country	India
Abstract	This paper presents an analytical study on strategies to increase engagement and follower growth across personal accounts (individuals, lifestyle, creators) versus professional accounts (businesses, brands, and organizations) on social media platforms such as Instagram, Twitter, and LinkedIn. Using a social media analytics framework, we compare content strategies, posting frequency, timing, audience targeting, and engagement metrics. Data was drawn from 50 real-world case studies, industry reports, and platform-level analytics. Findings reveal that personal accounts benefit from authenticity, storytelling, and interactive features, while professional accounts gain from consistent branding, niche targeting, and value-driven content. Comparative results indicate that engagement rates are higher on personal accounts (average 5–7%) compared to professional accounts (2–4%), but follower growth is more sustainable in professional contexts due to structured campaigns. Recommendations include integrating AI-powered analytics dashboards, A/B testing, and sentiment analysis tools to refine strategies.
Keywords	Social Media Analytics; Engagement Strategies; Follower Growth; Personal Accounts; Professional Accounts.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61841
Short DOI	https://doi.org/hbdr6z

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