Utsav, JethvaJethvaUtsavKabaria, DhaiwatDhaiwatKabariaVajpeyi, RibhuRibhuVajpeyiMina, MohitMohitMinaSrivastava, VivekVivekSrivastava2025-08-312025-08-312020-01-05[9781450377386]10.1145/3371158.33712262-s2.0-85078468175https://d8.irins.org/handle/IITG2025/24249Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.falseConference Paper359-3605 January 20205cpConference Proceeding2