@inproceedings{kongqiang-peng-2025-wangkongqiang,
    title = "wangkongqiang@{CASE} 2025: Detection and Classifying Language and Targets of Hate Speech using Auxiliary Text Supervised Learning",
    author = "Kongqiang, Wang  and
      Peng, Zhang",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Thapa, Surendrabikram},
    booktitle = "Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.case-1.7/",
    pages = "62--70",
    abstract = "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."
}