Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and...

Area of Science:

• Computational social science
• Machine learning applications in public health
• Social media analytics

Background:

• Social media is vital for public health surveillance and education.
• Limited demographic data on platforms like Reddit hinders targeted outreach.
• Machine learning models exist for Twitter but not extensively for Reddit users.

Purpose of the Study:

• To develop a machine learning algorithm for predicting Reddit user age segments (adolescents vs. adults).
• To utilize publicly available Reddit data for age prediction.
• To enhance public health's ability to identify and engage specific age demographics on Reddit.

Main Methods:

• Collected and manually labeled Reddit user ages from self-reported posts (Jan-Sep 2020).
• Engineered features from posts, comments, and metadata capturing linguistic patterns and user behavior.
• Trained and evaluated multiple classification algorithms, selecting the best performing model using 5-fold cross-validation.

Main Results:

• A gradient boosted trees classifier achieved an F1 score of 0.78.
• The model demonstrated strong precision (0.79) and recall (0.89) for adolescents.
• Key distinguishing features included sentence count per comment, recent account creation, and subreddit activity (e.g., r/teenagers).

Conclusions:

• An accurate Reddit age prediction algorithm was developed using publicly available data.
• Machine learning can assist public health agencies in identifying age-specific audiences on Reddit.
• Distinct posting behaviors, linguistic styles, and account characteristics differentiate adolescent and adult Reddit users.