Our papers

Rationale-Guided Few-Shot Classification to Detect Abusive Language

Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain …

On the rise of fear speech in online social media

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle …

HateMM: A Multi-Modal Dataset for Hate Video Classification

Hate speech has become one of the most significant issues inmodern society, having implications in both the online and theoffline world. Due to this, hate speech research has recentlygained a lot of traction. However, most of the work has pri-marily …

Multilingual Abusive Comment Detection at Scale for Indic Languages

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one …

Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments

The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. …

CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech

Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation …

Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages

Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace …

HateCheckHIn: Evaluating Hindi Hate Speech Detection Models

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one …

Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach

Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. They also employ moderators who can check the posts violating moderation …

Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages

Hate speech is considered to be one of the major issues currently plaguing online social media. Repeated and repetitive exposure to hate speech has been shown to create physiological effects on the target users. Thus, hate speech, in all its forms, …