Detecting Fake Users on Social Media with a Graph Database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media has become a major part of people’s daily lives as it provides users with the convenience to connect with people, interact with friends, share personal content with others, and gather information. However, it also creates opportunities for fake users. Fake users on social media may be perceived as popular and influential if not detected. They might spread false information or fake news by making it look real, manipulating real users into making certain decisions. In computer science, a social network can be treated as a graph, which is a data structure consisting of nodes being the social media users, and edges being the connections between users. Graph data can be stored in a graph database for efficient data analysis. In this paper, we propose using a graph database to achieve an increased scalability to accommodate larger graphs. Centrality measures as features were extracted for the random forest classifier to successfully detect fake users with high precision, recall, and accuracy. We have achieved promising results especially when compared with previous studies. 
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 it