A Hierarchically-Labeled Portuguese Hate Speech Dataset

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Paula Fortuna (Author) Other; João Rocha da Silva (Author) FEUP; Juan Soler-Company (Author) Other; Leo Wanner (Author) Other; Sérgio Nunes (Author) FEUP

Conference proceedings International

Title: THIRD WORKSHOP ON ABUSIVE LANGUAGE ONLINE

Pages: 94-104

3rd Workshop on Abusive Language Online

Florence, ITALY, AUG 01, 2019

Indexing

ISI Web of Knowledge - 17 Citations

Other information

Authenticus ID: P-00S-AEM

Abstract (EN): Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning is applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. First, non-experts annotated the tweets with binary labels ('hate' vs. 'no-hate'). Then, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. The hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11