Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments

H Madhu, S Satapara, S Modha, T Mandl… - Expert Systems with …, 2023 - Elsevier
Abstract The spread of Hate Speech on online platforms is a severe issue for societies and
requires the identification of offensive content by platforms. Research has modeled Hate …

[PDF][PDF] Lidoma at homomex2023@ iberlef: Hate speech detection towards the mexican spanish-speaking lgbt+ population. the importance of preprocessing before …

M Shahiki-Tash, J Armenta-Segura, Z Ahani… - Proceedings of the …, 2023 - ceur-ws.org
Hate speech targeting LGBT+ individuals poses a deeply ingrained problem with wide-
ranging consequences, encompassing substance abuse disorders and discrimination …

Robustness of models addressing Information Disorder: A comprehensive review and benchmarking study

G Fenza, V Loia, C Stanzione, M Di Gisi - Neurocomputing, 2024 - Elsevier
Abstract Machine learning and deep learning models are increasingly susceptible to
adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder …

A Transformer Based Approach for Abuse Detection in Code Mixed Indic Languages.

V Bansal, M Tyagi, R Sharma, V Gupta… - ACM transactions on Asian …, 2022 - dl.acm.org
The advancement in the number of online social media platforms has entailed active
participation from the web users globally. This has also lead to subsequent increase in the …

[PDF][PDF] Overview of the HASOC Subtrack at FIRE 2022: Identification of Conversational Hate-Speech in Hindi-English Code-Mixed and German Language.

S Modha, T Mandl, P Majumder, S Satapara… - FIRE (Working …, 2022 - researchgate.net
This article provides an overview of a shared task to identify contextual hate speech in social
media conversations. This task intends to analyze how context within a conversation in …

CoSyn: Detecting implicit hate speech in online conversations using a context synergized hyperbolic network

S Ghosh, M Suri, P Chiniya, U Tyagi, S Kumar… - arXiv preprint arXiv …, 2023 - arxiv.org
The tremendous growth of social media users interacting in online conversations has led to
significant growth in hate speech, affecting people from various demographics. Most of the …

Cordyceps@ LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding

D Ninalga - arXiv preprint arXiv:2309.13561, 2023 - arxiv.org
Detecting transphobia, homophobia, and various other forms of hate speech is difficult.
Signals can vary depending on factors such as language, culture, geographical region, and …

Hate speech is not free speech: Explainable machine learning for hate speech detection in code-mixed languages

S Yadav, A Kaushik, K McDaid - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
The increase in connectivity provided by social media platforms comes with several
disadvantages. It has become surprisingly easy for ill-intentioned individuals to stalk, harass …

[PDF][PDF] Baseline BERT models for Conversational Hate Speech Detection in Code-mixed tweets utilizing Data Augmentation and Offensive Language Identification in …

K Ghosh, A Senapati, U Garain - FIRE (Working Notes), 2022 - ceur-ws.org
In today's world, social media plays a vital role in spreading hate towards a person or group
based on their color, caste, sex, sexual orientation, political differences, etc. Most of the work …

CUET_NLP_Manning@ LT-EDI 2024: Transformer-based Approach on Caste and Migration Hate Speech Detection

M Alam, HMA Taher, J Hossain, S Ahsan… - Proceedings of the …, 2024 - aclanthology.org
The widespread use of online communication has caused a significant increase in the
spread of hate speech on social media. However, there are also hate crimes based on caste …