Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

2021 ◽  
pp. 312-326
Author(s):  
Raúl R. Romero-Vega ◽  
Oscar M. Cumbicus-Pineda ◽  
Ruperto A. López-Lapo ◽  
Lisset A. Neyra-Romero
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech

Detection of Cyberbullying on Social Media Using Machine learning

2021 ◽  
Vol 9 (10) ◽  
pp. 1401-1409
Author(s):  
Mitta Roja
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Text Data ◽  
Model Based

Abstract: Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platorms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Threemethods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it givesaccuracies above 80%. Keywords: Cyberbullying, Hate speech, Personal attacks,Machine learning, Feature extraction, Twitter, Wikipedia

Identification of Hate Speech using Natural Language Processing and Machine Learning

2021 ◽  
Author(s):  
Darsh Bhimani ◽  
Rutvi Bheda ◽  
Femin Dharamshi ◽  
Deepti Nikumbh ◽  
Priyanka Abhyankar
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech

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.

scholarly journals Text Analyzer - An Approach for Hate Speech & Offensive Language Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 589-593
Author(s):  
Dr. Sweeta Bansal
Keyword(s):  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
The Social ◽  
Logistic Regression Algorithm ◽  
Offensive Language ◽  
Language Detection ◽  
Day By Day

As we know that the social crowd is increasing day by day, so is the hatred among them online. This hatred gives rise to hate speech/comments that are passed to one another online. Recently, the hate speech has increased so much that we need a way to stop them or at least contain it to minimum. Keeping this problem in mind, we have introduced a way in which we can detect the class of comments that are posted online and stop its spread if it belongs to hateful category. We have used Natural Language Processing methods and Logistic Regression algorithm to achieve our goal.

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