Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach

Computing ◽  
2019 ◽  
Vol 102 (2) ◽  
pp. 501-522 ◽  
Author(s):  
Zafer Al-Makhadmeh ◽  
Amr Tolba
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Learning Approach ◽  
Speech Detection

Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation

2021 ◽  
Author(s):  
K.G.C.M Kooragama ◽  
L.R.W.D. Jayashanka ◽  
J.A. Munasinghe ◽  
K.W. Jayawardana ◽  
Muditha Tissera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Approach ◽  
Speech Evaluation

Research Journey of Hate Content Detection From Cyberspace

2021 ◽  
pp. 200-225
Author(s):  
Sayani Ghosal ◽  
Amita Jain
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Digital Media ◽  
Language Processing ◽  
Social Networking Sites ◽  
Hate Speech ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Speech Detection ◽  
Healthy Environment

Hate content detection is the most prospective and challenging research area under the natural language processing domain. Hate speech abuse individuals or groups of people based on religion, caste, language, or sex. Enormous growth of digital media and cyberspace has encouraged researchers to work on hatred speech detection. A commonly acceptable automatic hate detection system is required to stop flowing hate-motivated data. Anonymous hate content is affecting the young generation and adults on social networking sites. Through numerous studies and review papers, the chapter identifies the need for artificial intelligence (AI) in hate speech research. The chapter explores the current state-of-the-art and prospects of AI in natural language processing (NLP) and machine learning algorithms. The chapter aims to identify the most successful methods or techniques for hate speech detection to date. Revolution in this research helps social media to provide a healthy environment for everyone.

scholarly journals Multimodal Hate Speech Detection in Greek Social Media

2021 ◽  
Vol 5 (7) ◽  
pp. 34
Author(s):  
Konstantinos Perifanos ◽  
Dionysis Goutsos
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Language Model ◽  
Fine Tuning ◽  
Accuracy Score ◽  
Speech Detection ◽  
Online Social Media

Hateful and abusive speech presents a major challenge for all online social media platforms. Recent advances in Natural Language Processing and Natural Language Understanding allow for more accurate detection of hate speech in textual streams. This study presents a new multimodal approach to hate speech detection by combining Computer Vision and Natural Language processing models for abusive context detection. Our study focuses on Twitter messages and, more specifically, on hateful, xenophobic, and racist speech in Greek aimed at refugees and migrants. In our approach, we combine transfer learning and fine-tuning of Bidirectional Encoder Representations from Transformers (BERT) and Residual Neural Networks (Resnet). Our contribution includes the development of a new dataset for hate speech classification, consisting of tweet IDs, along with the code to obtain their visual appearance, as they would have been rendered in a web browser. We have also released a pre-trained Language Model trained on Greek tweets, which has been used in our experiments. We report a consistently high level of accuracy (accuracy score = 0.970, f1-score = 0.947 in our best model) in racist and xenophobic speech detection.

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