Our papers

HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches ...

Exploring the Limits of Zero Shot Vision Language Models for Hate Meme Detection: The Vulnerabilities and their Interpretations

There is a rapid increase in the use of multimedia content in current social media platforms. One of the highly popular forms of such multimedia content are memes. While memes have been primarily invented to promote funny and buoyant ...

Mitigation

In the previous sections, the authors first explored how machine learning can be used to detect hate speech and later aimed to make the detection more explainable. This makes the moderation system is more reliable. In this section, the authors ...

Detection

This chapter will discuss the techniques developed over time for hate speech detection. We will explore how hate speech detection techniques have evolved, ranging from keyword-based methods and machine learning techniques to deep learning ...

Dynamics of Toxicity in Political Podcasts

Toxicity in digital media poses significant challenges, yet little attention has been given to its dynamics within the rapidly growing medium of podcasts. This paper addresses this gap by analyzing political podcast data to study the emergence ...

Multilingual and Explainable Text Detoxification with Parallel Corpora

Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022, digital abusive speech remains a significant issue. One potential ...

CrowdCounter: A benchmark type-specific multi-target counterspeech dataset

Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. However, writing effective counterspeech is challenging for moderators/users. Hence, developing suggestion tools ...

On Zero-Shot Counterspeech Generation by LLMs

With the emergence of numerous Large Language Models (LLM), the usage of such models in various Natural Language Processing (NLP) applications is increasing extensively. Counterspeech generation is one such key task where efforts are made to develop ...

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, …