Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments
Bibliographic record
Abstract
With the proliferation of social media platforms like Instagram, Twitter, and Facebook, the dissemination of information has undergone a significant transformation.Instagram, distinguished by its emphasis on visual media, has emerged as a platform of choice for photo and video sharing.Despite its popularity, the platform's vast reach renders it vulnerable to malevolent activities, including cyberbullying.While prior research has employed SVM, NBC, C45, and K-Nearest Neighbors for cyberbullying analysis, these studies predominantly focused on Twitter.This paper presents a novel approach, harnessing the power of K-means Clustering to identify instances of cyberbullying on Instagram.In this study, a labelled dataset is gathered and subjected to pre-processing steps, including case folding, tokenization, removal of stopwords, normalization, and stemming.Subsequently, the K-means Clustering algorithm is implemented and evaluated using 10fold cross-validation.The results indicate a threshold value of 1.0, an accuracy rate of 64.25%, a precision of 79.29%, and a recall of 59.88% in categorizing bullying words on Instagram.This research underscores the potential of the K-means algorithm in effectively distinguishing between bullying and non-bullying comments.A notable advancement of this paper is the integration of the two tf-idf weighting methods with the K-means clustering algorithm, thereby enhancing the accuracy in grouping comment data into cyberbullying and non-cyberbullying categories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".