scholarly journals Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

2022 ◽  
Vol 29 (1) ◽  
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Online Social Networks ◽  
Hate Speech ◽  
Automatic Detection ◽  
Learning Models ◽  
Novel Approach

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

2021 ◽  
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2664
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

scholarly journals Misinformation, Radicalization and Hate Through the Lens of Users

2020 ◽  
Author(s):  
Manoel Horta Ribeiro ◽  
Virgílio A. F. Almeida ◽  
Wagner Meira Jr
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Network Structure ◽  
Online Social Networks ◽  
Hate Speech ◽  
Far Right ◽  
Content Creation ◽  
Political Opinion

The popularization of Online Social Networks has changed the dynamics of content creation and consumption. In this setting, society has witnessed an amplification in phenomena such as misinformation and hate speech. This dissertation studies these issues through the lens of users. In three case studies in social networks, we: (i) provide insight on how the perception of what is misinformation is altered by political opinion; (ii) propose a methodology to study hate speech on a user-level, showing that the network structure of users can improve the detection of the phenomenon; (iii) characterize user radicalization in far-right channels on YouTube through time, showing a growing migration towards the consumption of extreme content in the platform.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Deep learning-based personality recognition from text posts of online social networks

2018 ◽  
Vol 48 (11) ◽  
pp. 4232-4246 ◽  
Author(s):  
Di Xue ◽  
Lifa Wu ◽  
Zheng Hong ◽  
Shize Guo ◽  
Liang Gao ◽  
...  
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Online Social Networks ◽  
Personality Recognition

Fake news detection using deep learning models: A novel approach

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Sachin Kumar ◽  
Rohan Asthana ◽  
Shashwat Upadhyay ◽  
Nidhi Upreti ◽  
Mohammad Akbar
Keyword(s):  
Deep Learning ◽  
Fake News ◽  
Learning Models ◽  
Novel Approach

scholarly journals A ML and NLP based Framework for Sentiment Analysis on Bigdata

2020 ◽  
Vol 8 (5) ◽  
pp. 189-200
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Online Social Networks ◽  
Topic Model ◽  
Social Feedback ◽  
Feedback Systems ◽  
Multiple Sources ◽  
Probabilistic Topic Model ◽  
Novel Approach

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.

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