We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning.
- Authors
Aljuhani, Khulood O.; Alyoubi, Khaled H.; Alotaibi, Fahd S.
- Abstract
In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings.
- Subjects
DEEP learning; ONLINE social networks; ARABIC language; MICROBLOGS; NATURAL language processing; SOCIAL media
- Publication
Technical Journal / Tehnički Glasnik, 2022, Vol 16, Issue 3, p394
- ISSN
1846-6168
- Publication type
Article
- DOI
10.31803/tg-20220305120018