Social media platforms, while enabling global connectivity, have become hubs for the rapid spread of harmful content, including hate speech and fake narratives \cite{davidson2017automated, shu2017fake}. The Faux-Hate shared task focuses on detecting a specific phenomenon: the generation of hate speech driven by fake narratives, termed Faux-Hate. Participants are challenged to identify such instances in code-mixed Hindi-English social media text. This paper describes our system developed for the shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involving fake and hate speech classification, and (b) Target and Severity prediction, categorizing the intended target and severity of hateful content. Our approach combines advanced natural language processing techniques with domain-specific pretraining to enhance performance across both tasks. The system achieved competitive results, demonstrating the efficacy of leveraging multi-task learning for this complex problem.

翻译：社交媒体平台在实现全球互联的同时，已成为有害内容（包括仇恨言论和虚假叙事）快速传播的中心 \\cite{davidson2017automated, shu2017fake}。Faux-Hate共享任务专注于检测一种特定现象：由虚假叙事驱动的仇恨言论生成，称为Faux-Hate。参与者面临的挑战是在印地语-英语混合编码的社交媒体文本中识别此类实例。本文描述了为该共享任务开发的系统，该系统处理两个主要子任务：(a) 二元Faux-Hate检测，涉及虚假和仇恨言论分类；(b) 目标与严重性预测，对仇恨内容的目标对象和严重程度进行分类。我们的方法结合了先进的自然语言处理技术与领域特定的预训练，以提升两项任务的性能。该系统取得了具有竞争力的结果，证明了利用多任务学习解决这一复杂问题的有效性。