Cyber-Troll Detection using Deep Learning and NLP: A Comparative Study
Bibliographic record
Abstract
The proliferation of malicious online behaviors, particularly cyber-trolling, presents significant challenges to maintaining healthy online communities. This paper investigates the efficacy of four deep learning architectures-BERT, LSTM, GRU, and Causal Convolutional Networks (Causal Conv 1D)-for the automatic detection of cyber-trolls based on textual content. Using a comprehensive dataset of 50,000 social media comments, we evaluate these models on their ability to distinguish between normal users and trolls. Our results indicate that while the pre-trained BERT model achieves the highest overall accuracy ($94.2 \%$), the Causal Conv 1D architecture demonstrates competitive performance $(92.7 \%)$ with significantly lower computational requirements. We also analyze the semantic features that most effectively contribute to troll detection and discuss the ethical implications of automated moderation systems. This research contributes to the development of more efficient and effective methods for maintaining civil discourse in online 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".