Facebook Metrics for Engagement Analysis

Facebook Metrics is a dataset from the UCI Machine Learning Repository. It contains data related to social media posts, user interactions, and marketing performance. The dataset is intended for analysis of engagement patterns on the Facebook platform.

Use Cases

• Predict post engagement levels using features like like_count, comment_count, and share_count.
• Analyze the relationship between post_type or category and user interaction metrics.
• Model the lifetime or reach of content based on timestamp and page_total_likes.
• Cluster posts into performance tiers using features such as total_interactions and post_weekday.

Strengths

• Sourced from the authoritative UCI Machine Learning Repository.
• Focuses on concrete social media interaction metrics.

Limitations

• Specific row count, column names, and data completeness are unknown.
• The temporal range and geographic scope of the data are unspecified.
• Potential for platform-specific bias, limited to Facebook's ecosystem.

Provenance

Source

UCI Machine Learning Repository

Collection Method

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Time Range

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Freshness

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Geography

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