Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities
Bibliographic record
Abstract
Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:”online harassment”, “vulgar language” and “valance” and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.
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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 | 0.000 | 0.003 |
| 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 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".