Knowing the origin will help in finding the specific "deep paper" or documentation you need.
: Researchers frequently use Random Forest models to analyze large-scale CSV/XLSX exports of Facebook data to predict user attributes like age, gender, or political leaning. 100K RF FACEBOOK.xlsx
If your interest is in the algorithm itself applied to this scale: Knowing the origin will help in finding the
Papers in this category often use datasets of 100K+ users to predict psychological traits or engagement. : Private Traits and Attributes are Predictable from
: Private Traits and Attributes are Predictable from Digital Records of Human Behavior (PNCAS). 2. Marketing & Reach Frequency (RF) Modeling
: Optimizing Facebook ad campaigns using Random Forest for ROI prediction.
: Many datasets labeled "100K" are used to train classifiers (like RF) to detect spam or misinformation on Facebook. Key Source : Detecting Fake News on Social Media (ACM) . 4. Technical Specification: Random Forest (RF)
