Explorative Application of Fusion Techniques for Multimodal Hate Speech Detection

2022 ◽  
Vol 3 (2) ◽  
Author(s):  
Siva Sai ◽  
Naman Deep Srivastava ◽  
Yashvardhan Sharma
Keyword(s):  
Hate Speech ◽  
Speech Detection

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.

scholarly journals To BAN or Not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection

2021 ◽  
Author(s):  
Kristian Miok ◽  
Blaž Škrlj ◽  
Daniela Zaharie ◽  
Marko Robnik-Šikonja
Keyword(s):  
Monte Carlo ◽  
Hate Speech ◽  
Classification Performance ◽  
Reliability Estimation ◽  
Superior Performance ◽  
Speech Detection ◽  
Attention Networks ◽  
Reliability Estimates ◽  
Viable Mechanism ◽  
Affective Dimensions

AbstractHate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the results of the proposed approach on hate speech detection problems in several languages. Additionally, we test whether affective dimensions can enhance the information extracted by the BERT model in hate speech classification. Our experiments show that Monte Carlo dropout provides a viable mechanism for reliability estimation in transformer networks. Used within the BERT model, it offers state-of-the-art classification performance and can detect less trusted predictions.

scholarly journals A Multilingual Evaluation for Online Hate Speech Detection

2020 ◽  
Vol 20 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Michele Corazza ◽  
Stefano Menini ◽  
Elena Cabrio ◽  
Sara Tonelli ◽  
Serena Villata
Keyword(s):  
Hate Speech ◽  
Speech Detection
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