4.3 Article

Detection of Offensive Language and ITS Severity for Low Resource Language

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3580476

Keywords

Hate speech; long short-term memory; Urdu NLP; convolutional neural network; BERT

Ask authors/readers for more resources

Continuous proliferation of hate speech on social media has become a major concern for researchers, who emphasize the importance of detecting and classifying its severity. This study focuses on detecting offensive and hate speech in the Urdu language, specifically targeting religion, racism, and national origin. The severity of hate speech is categorized into symbolization, insult, and attribution. An annotated corpus of over 20,000 tweets is collected and various models are applied to achieve high F-scores in hate speech detection, showing promising results for further investigation.
Continuous proliferation of hate speech in different languages on social media has drawn significant attention from researchers in the past decade. Detecting hate speech is indispensable irrespective of the scale of use of language, as it inflicts huge harm on society. This work presents a first resource for classifying the severity of hate speech in addition to classifying offensive and hate speech content. Current research mostly limits hate speech classification to its primary categories, such as racism, sexism, and hatred of religions. However, hate speech targeted at different protected characteristics also manifests in different forms and intensities. It is important to understand varying severity levels of hate speech so that the most harmful cases of hate speech may be identified and dealt with earlier than the less harmful ones. In this work, we focus on detecting offensive speech, hate speech, and multiple levels of hate speech in the Urdu language. We investigate three primary target categories of hate speech: religion, racism, and national origin. We further divide these categories into levels based on the severity of hate conveyed. The severity levels are referred to as symbolization, insult, and attribution. A corpus comprising more than 20,000 tweets against the corresponding hate speech categories and severity levels is collected and annotated. A comprehensive experimentation scheme is applied using traditional as well as deep learning-based models to examine their impact on hate speech detection. The highest macro-averaged F-score yielded for detecting offensive speech is 86% while the highest F-scores for detecting hate speech with respect to ethnicity, national origin, and religious affiliation are 80%, 81%, and 72%, respectively. This shows that results are very encouraging and would provide a lead towards further investigation in this domain.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available