JL-Hate: An Annotated Dataset for Joint Learning of Hate Speech and Target Detection

Date

2024-01-01

Author

Buyukdemirci, Kaan
Kucukkaya, Izzet Emre
Olmez, Eren
Toraman, Çağrı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The detection of hate speech is a subject extensively explored by researchers, and machine learning algorithms play a crucial role in this domain. The existing resources mostly focus on text sequence classification for the task of hate speech detection. However, the target of hateful content is another dimension that has not been studied in details due to the lack of data resources. In this study, we address this gap by introducing a novel tweet dataset for the task of joint learning of hate speech detection and target detection, called JL-Hate, for the tasks of sequential text classification and token classification, respectively. The JL-Hate dataset consists of 1,530 tweets divided equally in English and Turkish languages. Leveraging this dataset, we conduct a series of benchmark experiments. We utilize a joint learning model to concurrently perform sequence and token classification tasks on our data. Our experimental results demonstrate consistent performance with the prevalent studies, both in sequence and token classification tasks.

Subject Keywords

Hate speech detection, Joint learning, Target detection

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195953165&origin=inward
https://hdl.handle.net/11511/110240

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Department of Computer Engineering, Conference / Seminar