Neural Network Applications in Hate Speech Detection

2020 ◽  
pp. 188-204
Author(s):  
Brian Tuan Khieu ◽  
Melody Moh
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Hate Speech ◽  
Speech Detection ◽  
Network Applications ◽  
Current State ◽  
New Directions ◽  
Key Techniques ◽  
Neural Network Applications ◽  
Positive Results

This chapter presents a literature survey of the current state of hate speech detection models with a focus on neural network applications in the area. The growth and freedom of social media has facilitated the dissemination of positive and negative ideas. Proponents of hate speech are one of the key abusers of the privileges allotted by social media, and the companies behind these networks have a vested interest in identifying such speech. Manual moderation is too cumbersome and slow to deal with the torrent of content generation on these social media sites, which is why many have turned to machine learning. Neural network applications in this area have been very promising and yielded positive results. However, there are newly discovered and unaddressed problems with the current state of hate speech detection. Authors' survey identifies the key techniques and methods used in identifying hate speech, and they discuss promising new directions for the field as well as newly identified issues.

scholarly journals Bangla hate speech detection on social media using attention-based recurrent neural network

2021 ◽  
Vol 30 (1) ◽  
pp. 578-591
Author(s):  
Amit Kumar Das ◽  
Abdullah Al Asif ◽  
Anik Paul ◽  
Md. Nur Hossain
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Hate Speech ◽  
Negative Aspect ◽  
Speech Detection ◽  
Use Of Technology ◽  
Machine Learning Model ◽  
Bengali Language ◽  
Facebook Pages

Abstract Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%).

Neural Network Applications: A Literature Review

1992 ◽  
Author(s):  
Vince L. Wiggins ◽  
Sheree K. Engquist ◽  
Larry T. Looper
Keyword(s):  
Neural Network ◽  
Literature Review ◽  
Network Applications ◽  
Neural Network Applications

scholarly journals A Web Interface for Analyzing Hate Speech

2021 ◽  
Vol 13 (3) ◽  
pp. 80
Author(s):  
Lazaros Vrysis ◽  
Nikolaos Vryzas ◽  
Rigas Kotsakis ◽  
Theodora Saridou ◽  
Maria Matsiola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Graphical User Interface ◽  
Hate Speech ◽  
Web Interface ◽  
Learning Models ◽  
Speech Detection ◽  
Media Services ◽  
The Web ◽  
Machine Learning Models

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

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