100k Rf Facebook.xlsx May 2026

Papers in this category often use datasets of 100K+ users to predict psychological traits or engagement.

If your interest is in the algorithm itself applied to this scale:

: 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)

: 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.

Based on the components of the filename, this topic likely involves using a machine learning model—a robust algorithm for classification and regression—trained on a dataset of 100,000 (100K) samples related to Facebook (likely social media metrics, user behavior, or advertising data).