scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2020 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin ◽  
Shamima Parvez ◽  
Mohammad Motiur Rahman ◽  
Ammar Alazab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2021 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

scholarly journals Effective Compatibility and Reduction of Data for Bigdata Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 3781-3784
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Language Processing ◽  
Processing Technique ◽  
Unstructured Data ◽  
Machine Learning Method ◽  
Learning Method ◽  
Source File ◽  
Natural Language Processing Technique ◽  
Input And Output

The system identifies a duplicate record from the database using the machine learning method. We must pass unstructured data. Data are prepared using any natural language processing technique such as text similarity. This prepared data is then fed into the latest machine learning method called Random Forest. After this data collection, using these files, the target file is compared to the source file. We make input and output files. This is carried out until accurate efficiency is generated

scholarly journals Review of the machine learning methods in the classification of phishing attack

2019 ◽  
Vol 8 (4) ◽  
pp. 1545-1555
Author(s):  
John Arthur Jupin ◽  
Tole Sutikno ◽  
Mohd Arfian Ismail ◽  
Mohd Saberi Mohamad ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Computer Network ◽  
Computer Users ◽  
Machine Learning Method ◽  
Learning Method ◽  
Phishing Attacks ◽  
Internet Users ◽  
Internet Networks ◽  
Use Of Internet

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.

scholarly journals A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

2021 ◽  
Vol 4 (1) ◽  
pp. 01-26
Author(s):  
Muhammad Arif
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Social Media Networks ◽  
Current State ◽  
Art Research ◽  
Cyberbullying Detection

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

scholarly journals Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information

2021 ◽  
Vol 5 (2) ◽  
pp. 415
Author(s):  
Firdausi Nuzula Zamzami ◽  
Adiwijaya Adiwijaya ◽  
Mahendra Dwifebri P
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Mutual Information ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Internet Information

Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.

scholarly journals Could a Conversational AI Identify Offensive Language?

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 418
Author(s):  
Daniela America da Silva ◽  
Henrique Duarte Borges Louro ◽  
Gildarcio Sousa Goncalves ◽  
Johnny Cardoso Marques ◽  
Luiz Alberto Vieira Dias ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Unethical Behavior ◽  
Historical Data ◽  
Language Models ◽  
The Internet ◽  
Proof Of Concept ◽  
Offensive Language

In recent years, we have seen a wide use of Artificial Intelligence (AI) applications in the Internet and everywhere. Natural Language Processing and Machine Learning are important sub-fields of AI that have made Chatbots and Conversational AI applications possible. Those algorithms are built based on historical data in order to create language models, however historical data could be intrinsically discriminatory. This article investigates whether a Conversational AI could identify offensive language and it will show how large language models often produce quite a bit of unethical behavior because of bias in the historical data. Our low-level proof-of-concept will present the challenges to detect offensive language in social media and it will discuss some steps to propitiate strong results in the detection of offensive language and unethical behavior using a Conversational AI.

scholarly journals Identifying psychological indexes based on social media data: A machine learning method

2021 ◽  
Vol 29 (4) ◽  
pp. 571
Author(s):  
Yue SU ◽  
Mingming LIU ◽  
Nan ZHAO ◽  
Xiaoqian LIU ◽  
Tingshao ZHU
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Method ◽  
Learning Method ◽  
Social Media Data ◽  
Media Data
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