Graph Convolutional Networks for Categorizing Online Harassment on Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twitter is one of the social media platforms that people express themselves freely. Harassment is one consequence of these such platforms, which is hard to obstruct. Text categorization and classification is a task that aims to solve this problem. Several studies applied classical machine learning methods and recent deep neural networks to categorize the text. However, only a few studies have explored graph convolutional neural networks while using classical approaches to categorize harassment Tweets. In this work, we propose using graph convolutional networks (GCN) for tweet categorization. Second, we explore this categorization task using classical machine learning approaches and compare the results with the GCN model. Third, we show the effectiveness of the GCN model on this problem by the other evaluation of the model on fewer sample datasets. In addition, we used different embedding approaches to find the best representation for the dataset in each of the models and represent the best embedding approach to use in this problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it