Detecting Cyberbullying on Social Media Using Support Vector Machine: A Case Study on Twitter
Bibliographic record
Abstract
Cyberbullying, a prevalent issue in digital media, particularly social media, poses a significant concern owing to its pervasive nature and potential harm.Social media platforms permit users to exchange opinions freely, which, while fostering open discourse, can also trigger instances of cyberbullying.This study focusses on Twitter discourses related to Indonesia's contentious public policy, "Cipta Kerja".The inherent polarity of views towards this policy has given rise to instances of cyberbullying.An extensive dataset comprising 2400 tweets was meticulously assembled, employing the keyword "Cipta Kerja".This dataset was subsequently partitioned into training and testing subsets to facilitate cyberbullying detection through computational algorithms.Sentiment analysis played a crucial role in this process, with the Support Vector Machine (SVM) method demonstrating remarkable reliability in classifying sentiment-related issues and, therefore, detecting cyberbullyings.The SVM method, using a linear kernel function, achieved a commendable accuracy rate of 92.7% in cyberbullying detection.This study's results underscore the effectiveness of SVM in identifying instances of cyberbullying on social media platforms, offering new promise for safeguarding digital spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".