Application of LSTM and GloVe Word Embedding for Hate Speech Detection in Indonesian Twitter Data
Bibliographic record
Abstract
Hate speech, characterized by intentional expressions of dissatisfaction, is a prevalent phenomenon on social media platforms, including Twitter.Its continual occurrence can foster divisions, misunderstandings, and even acts of violence between individuals and groups, particularly due to the resulting prejudice.This study investigates the occurrence of hate speech within Indonesian content on Twitter, employing a deep learning approach to detect and analyze such expressions.The Long Short-Term Memory (LSTM) method, coupled with the GloVe word embedding technique, is utilized on a dataset comprising 13,169 Indonesian tweets flagged for hate speech.Four distinct model architectures were developed through the integration of LSTM and GloVe.The findings reveal model 1 to exhibit superior performance, achieving a precision of 89%, a recall of 99%, an F-1 score of 94%, and an overall accuracy of 94.24%.It is suggested that future research explore the potential deployment of this model in web or mobile platforms for real-time analysis, thereby enhancing the capacity for immediate hate speech detection and mitigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".