Hateful online content is a growing concern, especially for young people. While social media platforms aim to connect us, they can also become breeding grounds for negativity and harmful language. This study tackles this issue by proposing a novel framework called HOLD-Z, specifically designed to detect hate and offensive comments in Telugu-English code-mixed social media content. HOLD-Z leverages a combination of approaches, including three powerful models: LSTM architecture, Zypher, and openchat_3.5. The study highlights the effectiveness of prompt engineering and Quantized Low-Rank Adaptation (QLoRA) in boosting performance. Notably, HOLD-Z secured the 9th place in the prestigious HOLD-Telugu DravidianLangTech@EACL-2024 shared task, showcasing its potential for tackling the complexities of hate and offensive comment classification.

The rapid expansion of social media has led toan increase in code-mixed content, presentingsignificant challenges in the effective detectionof hate speech and fake narratives. To advanceresearch in this area, a shared task titled De-coding Fake Narratives in Spreading HatefulStories (Faux-Hate) was organized as part ofICON 2024. This paper introduces a multi-task learning model designed to classify Hindi-English code-mixed tweets into two distinct cat-egories: hate speech and false content. The pro-posed framework utilizes fastText embeddingsto create a shared feature space that adeptly cap-tures the semantic and syntactic intricacies ofcode-mixed text, including transliterated termsand out-of-vocabulary words. These sharedembeddings are then processed through twoindependent Support Vector Machine (SVM)classifiers, each specifically tailored for oneof the classification tasks. Our team, secured10th place among the participating teams, asevaluated by the organizers based on Macro F1scores.