Our team was interested in content classification and labeling from multimodal detection of Hate speech, Humor, and Stance in marginalized socio-political movement discourse. We joined the task: Subtask A-Detection of Hate Speech and Subtask B-Classifying the Targets of Hate Speech. In this two task, our goal is to assign a content classification label to multimodal Hate Speech. Detection of Hate Speech: The aim is to detect the presence of hate speech in the images. The dataset for this task will have binary labels: No Hate and Hate. Classifying the Targets of Hate Speech: Given that an image is hateful, the goal here is to identify the targets of hate speech. The dataset here will have four labels: Undirected, Individual, Community, and Organization. Our group used a supervised learning method and a text prediction model. The best result on the test set for Subtask-A and Subtask-B were F1 score of 0.6209 and 0.3453, ranking twentieth and thirteenth among all teams.