  Social media has become a bedrock for people to voice their opinions
worldwide. Due to the greater sense of freedom with the anonymity feature, it
is possible to disregard social etiquette online and attack others without
facing severe consequences, inevitably propagating hate speech. The current
measures to sift the online content and offset the hatred spread do not go far
enough. One factor contributing to this is the prevalence of regional languages
in social media and the paucity of language flexible hate speech detectors. The
proposed work focuses on analyzing hate speech in Hindi-English code-switched
language. Our method explores transformation techniques to capture precise text
representation. To contain the structure of data and yet use it with existing
algorithms, we developed MoH or Map Only Hindi, which means "Love" in Hindi.
MoH pipeline consists of language identification, Roman to Devanagari Hindi
transliteration using a knowledge base of Roman Hindi words. Finally, it
employs the fine-tuned Multilingual Bert and MuRIL language models. We
conducted several quantitative experiment studies on three datasets and
evaluated performance using Precision, Recall, and F1 metrics. The first
experiment studies MoH mapped text's performance with classical machine
learning models and shows an average increase of 13% in F1 scores. The second
compares the proposed work's scores with those of the baseline models and
offers a rise in performance by 6%. Finally, the third reaches the proposed MoH
technique with various data simulations using the existing transliteration
library. Here, MoH outperforms the rest by 15%. Our results demonstrate a
significant improvement in the state-of-the-art scores on all three datasets.
