Abuse Detection
30 papers with code • 0 benchmarks • 4 datasets
Abuse detection is the task of identifying abusive behaviors, such as hate speech, offensive language, sexism and racism, in utterances from social media platforms (Source: https://arxiv.org/abs/1802.00385).
Benchmarks
These leaderboards are used to track progress in Abuse Detection
Hate Speech and Offensive Language
Latest papers with no code
Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages
For the test set, approximately 1200 posts were provided.
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device
Together with a hybrid use of video image classification, the accuracy of child abuse detection can be significantly increased.
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems.
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
We argue that, due to the differences between tabular data and images or text, existing threat models are not suitable for tabular domains.
Enriching Abusive Language Detection with Community Context
Our paper highlights how community context can improve classification outcomes in abusive language detection.
Multilingual and Multimodal Abuse Detection
In this paper, we attempt abuse detection in conversational audio from a multimodal perspective in a multilingual social media setting.
The Online Behaviour of the Algerian Abusers in Social Media Networks
In this paper, we conduct a statistical study on the cyber-bullying and the abusive content in social media (i. e. Facebook), where we try to spot the online behaviour of the abusers in the Algerian community.
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning
We study the use of semi-supervised as well as supervised approaches for anomaly detection.
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack.
