: A "100K" dataset might contain performance metrics for 100,000 ad sets. The "RF" would refer to the Random Forest model used to determine which factors (bid price, creative, frequency) lead to the best conversion. 3. Fake News & Bot Detection
: Private Traits and Attributes are Predictable from Digital Records of Human Behavior (PNCAS). 2. Marketing & Reach Frequency (RF) Modeling
: Identifying 100,000 instances of automated or malicious accounts. 100K RF FACEBOOK.xlsx
If your interest is in the algorithm itself applied to this scale:
: Random Forest is preferred for 100K-row datasets because it handles high-dimensional data (many columns in an .xlsx) without the extensive preprocessing required by deep learning. : A "100K" dataset might contain performance metrics
: 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.
: Unlike "black box" deep learning, RF allows for "feature importance" analysis, showing exactly which Facebook metrics (e.g., shares vs. comments) are the strongest predictors. Fake News & Bot Detection : Private Traits
While the exact "deep paper" for that specific .xlsx file isn't publicly indexed, the following research areas represent the most likely "deep" academic context for such a dataset: 1. Facebook User Behavior & Prediction