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Machine Learning Models Predict Social Media Engagement Using Image Post Features

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning approach to predict social media engagement by analyzing visual, textual, and temporal features of image posts. The study focused on furniture firms' Facebook posts, extracting these features using text and image analytics. Several machine learning models, including Random Forest, LightGBM, and XGBoost, were evaluated to identify key drivers of engagement and their predictive power, offering recommendations for organizations. AI

IMPACT Provides a framework for optimizing social media content strategy through data-driven insights.

RANK_REASON The item is an academic paper detailing a machine learning methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine Learning Models Predict Social Media Engagement Using Image Post Features

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The item is an academic paper detailing a machine learning methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ritwik Singh, Mayukh Majumdar, Subodha Kumar ·

    Predicting Social Media Engagement using Machine Learning

    arXiv:2609.16082v1 Announce Type: cross Abstract: Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-qua…

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