Analyzing and Predicting Emotional Responses in Cyber Bullying Cases: A Deep Learning Approach
Bibliographic record
Abstract
Cyberbullying is a threat on any digital platform, and it can have a very harmful and emotional effect on the person receiving these types of comments. Here, we present a deep learning framework that utilizes NLP-based and neural network-based approaches to analyze and predict the emotional responses associated with cyberbullying incidents. The model is trained and evaluated using a curated dataset of social media posts labeled with emotions like anger, sadness, fear, and neutrality. Tokenization, lemmatization, and word embeddings (GloVe, BERT, etc.) are the different preprocessing methods used to represent textual data. Write. Multiple architectures, such as CNNs, LSTM networks, and transformer-based approaches, are compared to achieve high accuracy in emotional response classification. Experimental results show that transformer models outperform traditional learning models for precision and recall. The results can lead to intelligent monitoring systems that identify harmful emotional content followed by necessary, timely interventions. Such research shows promise for AI-driven emotion analysis to support safer online environments.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".