Predicting User Engagement of Facebook Post Images in Leading Universities: A Machine Learning Approach
Bibliographic record
Abstract
The objective of this study is to utilise machine learning for predicting user engagement with Facebook post images from universities, aiming to provide strategic guidance for their social media content.A comprehensive analysis was performed on a dataset comprising 1,200 images from 24 prominent universities in the United States, the United Kingdom, and Australia.The open-source machine learning software WEKA 3.8.5 was utilised, with three of its classifiers -J48, Sequential Minimal Optimisation (SMO), and Random Forest (RF) -chosen for their proven efficiency with high-dimensional data.These classifiers were trained using a unique mix of features from the Google Cloud Vision API and attributes derived from relevant literature.Cross-validation techniques were employed to assess the accuracy of the models.A robust performance was exhibited by all models: J48 yielded an accuracy of 90.92%, SMO 94.85%, and RF 97.08%.Even under 66/34 and 90/10 data splits, the high accuracy was sustained.These findings underscore the efficacy of machine learning models in predicting user engagement with Facebook post images for leading universities.Such techniques can be leveraged by universities to optimise their Facebook user engagement strategies, thereby informing the creation of content that aligns with their target audience preferences.This study illuminates promising directions for updating social media strategies in response to evolving communication trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".