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Register a new userImproving Cross-Domain Hate Speech Detection by Reducing the False Positive Rate
تحسين اكتشاف الكلام الكراهية عبر المجال عن طريق تقليل معدل إيجابي كاذب
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اكتشاف الكلام الكراهية هو مجال أبحاث بنشاط مع مجموعة متنوعة من الأساليب المقترحة مؤخرا التي سمحت بدفع النتائج الحديثة.واحدة من تحديات هذه الأساليب الآلية - وهي نماذج التعلم العميق الحديثة - خطر الإيجابيات الخاطئة (أي، اتهامات كاذبة)، والتي قد تؤدي إلى حجب أو إزالة محتوى وسائل التواصل الاجتماعي غير الضارة في التطبيقات مع تدخل المشرف القليلوبعدنحن نقيم نماذج التعلم العميق في حد سواء تحت ظروف الكشف عن الكلام في المجال والمجال عبر المجال، وإدخال نهج SVM يسمح بتحسين نتائج أحدث من الفن عند دمجها مع نماذج التعلم العميق من خلال غالبية بسيطةفرقة التصويت.يرجع التحسن أساسا إلى انخفاض معدل إيجابي كاذب.
Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results. One of the challenges of such automated approaches -- namely recent deep learning models -- is a risk of false positives (i.e., false accusations), which may lead to over-blocking or removal of harmless social media content in applications with little moderator intervention. We evaluate deep learning models both under in-domain and cross-domain hate speech detection conditions, and introduce an SVM approach that allows to significantly improve the state-of-the-art results when combined with the deep learning models through a simple majority-voting ensemble. The improvement is mainly due to a reduction of the false positive rate.
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We address the task of automatic hate speech detection for low-resource languages. Rather than collecting and annotating new hate speech data, we show how to use cross-lingual transfer learning to leverage already existing data from higher-resource l
anguages. Using bilingual word embeddings based classifiers we achieve good performance on the target language by training only on the source dataset. Using our transferred system we bootstrap on unlabeled target language data, improving the performance of standard cross-lingual transfer approaches. We use English as a high resource language and German as the target language for which only a small amount of annotated corpora are available. Our results indicate that cross-lingual transfer learning together with our approach to leverage additional unlabeled data is an effective way of achieving good performance on low-resource target languages without the need for any target-language annotations.
Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features. In hate speech detection, however, equalizing model predictions may
ignore important differences among targeted social groups, as hate speech can contain stereotypical language specific to each SGT. Here, to take the specific language about each SGT into account, we rely on counterfactual fairness and equalize predictions among counterfactuals, generated by changing the SGTs. Our method evaluates the similarity in sentence likelihoods (via pre-trained language models) among counterfactuals, to treat SGTs equally only within interchangeable contexts. By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.
In this paper, we describe experiments designed to evaluate the impact of stylometric and emotion-based features on hate speech detection: the task of classifying textual content into hate or non-hate speech classes. Our experiments are conducted for
three languages -- English, Slovene, and Dutch -- both in in-domain and cross-domain setups, and aim to investigate hate speech using features that model two linguistic phenomena: the writing style of hateful social media content operationalized as function word usage on the one hand, and emotion expression in hateful messages on the other hand. The results of experiments with features that model different combinations of these phenomena support our hypothesis that stylometric and emotion-based features are robust indicators of hate speech. Their contribution remains persistent with respect to domain and language variation. We show that the combination of features that model the targeted phenomena outperforms words and character n-gram features under cross-domain conditions, and provides a significant boost to deep learning models, which currently obtain the best results, when combined with them in an ensemble.
We present a system for zero-shot cross-lingual offensive language and hate speech classification. The system was trained on English datasets and tested on a task of detecting hate speech and offensive social media content in a number of languages wi
thout any additional training. Experiments show an impressive ability of both models to generalize from English to other languages. There is however an expected gap in performance between the tested cross-lingual models and the monolingual models. The best performing model (offensive content classifier) is available online as a REST API.
We study the usefulness of hateful metaphorsas features for the identification of the type and target of hate speech in Dutch Facebook comments. For this purpose, all hateful metaphors in the Dutch LiLaH corpus were annotated and interpreted in line
with Conceptual Metaphor Theory and Critical Metaphor Analysis. We provide SVM and BERT/RoBERTa results, and investigate the effect of different metaphor information encoding methods on hate speech type and target detection accuracy. The results of the conducted experiments show that hateful metaphor features improve model performance for the both tasks. To our knowledge, it is the first time that the effectiveness of hateful metaphors as an information source for hatespeech classification is investigated.
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