Fake News Detection is one of the most currently researched areas over the globe; many methods have come to light using different features as their sources. Hence, there are also methods using existing comments on any news article which can be used to determine the credibility of the news article as fake or real. Here, we have introduced a hypothesis that uses a machine learning approach to check the credibility of comments before they can be analyzed for further fake news detection. So, we have used various text classification algorithms to check for our hypothesis that filtering comments since there is a high possibility that the comments used for any analysis can be useless and full of useless stuff. For example, the comments showing only the emotion of readers like ‘Yesss’ or ‘Nooo!’ and likewise or the comments built using only the curse words. Such comments would prove useless as a contributing factor for fake news detection and might also affect the results of fake news detection for any news article. These text classifiers are—Complement Naive Bayes, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine. Out of these, the best accuracy is provided by the MultinomialNB method of 75.7% and Decision Tree with 75.4% as opposed to the original algorithm with an accuracy of 73.3% using the same dataset. Since the MultinomialNB has provided the best improvement in all the metrics compared to the original method, and we are focusing our paper on this method. This hypothesis aims to classify comments as junky (useless) comments and utility (useful) comments. These utility comments will be further used for analysis to identify fake news. Also, since the size of comments per article may vary from a few tens to a few hundred or thousands, we have used the semi-supervised approach to classify the comments in junky or utility comments classes. We have also collected data from various sources and collaborated them to fetch ourselves from a usable dataset. It contains 415 records with contents or article data for each record, along with many comments for each record. Moreover, we have also classified those comments into junky and utility comment classes using the basic definition of spam filtering. This can be improvised for different uses using different criteria. Hence, eradicating the useless comments and only analyzing the useful comments for better identification of fake news is fake news detection.

Comment Filtering Based Explainable Fake News Detection

The rapid advancement of social media has enabled people from all around the world to create, express and share their feelings easily and freely. It also generates voluminous data in various fields including health, education, finance and security etc. The huge amount of data is beneficial for research and analysis. In this study, a model based on algorithm of Random Forest and SVM is developed to classify the sentiment in social media comments. Nearly 100 thousand posts data on Chinese Language are taken from social media comments, then trained and tested using emotion theories as well as machine learning techniques. Each post would be rated based on the sentiment behind it on a scale of positive and negative. The model is later used to analyze specific incident to strengthen its accuracy, then cluster valuable comments. The study would facilitate the decision-making process and various measures can be taken to enhance the management of government.

Sentiment Analysis of Social Media Comments based on Random Forest and Support Vector Machine


 The authors consider in detail the identification of cyberbullying, which is carried out by fraudsters with the illegal use of the victim's personal data. Basically, the source of this information is social networks, e-mails. The use of social networks in society is growing exponentially on a daily basis. The use of social networks, in addition to numerous advantages, also has a negative character, namely, users face numerous cyber threats. Such threats include the use of personal data for criminal purposes, cyberbullying, cybercrime, phishing and cyberbullying. In this article, we will focus on the task of identifying trolls. Identifying trolls on social networks is a difficult task because they are dynamic in nature and are collected in several billion records. One of the possible solutions to identify trolls is the use of machine learning algorithms. The main contribution of the authors to the study of the topic is the use of the method of identifying trolls in social networks, which is based on the analysis of the emotional state of network users and behavioral activity. In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication. The distribution of users is carried out automatically through the use of a special type of neural networks, namely self-organizing Kohonen maps. The group number is also determined automatically. To determine the characteristics of users, on the basis of which the distribution into groups takes place, the number of comments, the average length of the comment and the indicator responsible for the emotional state of the user are used.


Identification of cyberbullying by neural network methods


 The Internet has emerged as a powerful infrastructure for worldwide communication and human interaction. Some unethical use of this technology spam, phishing, trolls, cyberbullying, viruses caused problems in the development of mechanisms that guarantee affordable and safe opportunities for its use. Currently, many studies are being conducted to detect spam and phishing. The detection of telephone fraud has become critically important, as it entails huge losses. Machine learning and natural language processing algorithms are used to analyze a huge amount of text data. Fraudsters are identified using text mining and can be implemented by analyzing the terms of a word or phrase. One of the difficult tasks is to divide this huge unstructured data into clusters. There are several thematic modeling models for these purposes. This article presents the application of these models, in particular LDA, LSI and NMF. A data set has been formed. A preliminary analysis of the data was carried out and signs were constructed for models in the task of recognizing the subject of the text. The approaches of keyword extraction in the tasks of text topic recognition are considered. The key concepts of these approaches are given. The disadvantages of these models are shown, and directions for improving text processing algorithms are proposed. The evaluation of the quality of the models was carried out. Improved models thanks to the selection of hyperparameters and changing the data preprocessing function.


Application of Thematic Modeling Methods in Text Topic Recognition Tasks to Detect Telephone Fraud

Nowadays internet-trolls have big impact on other users, it interferes with comfortable use. Objective of the project is creating model for identifying provocative and fake comments. Science articles about detection of trolls by hand were searched for making the criteria of relevant comments selection. To achieve the goal there were created two artificial neural networks: definition of sarcasm and definition of sentiment analysis. The program result is datasets of troll comments and fake comments, statistic and diagrams of definition.

Detection of Fake and Provokative Comments in Social Network Using Machine Learning

Modern transport systems in Russian megacities use smart cards with e-wallets and RFID tags for ticketing and fare management. They are convenient for both passengers and conductors. But how safe are transport cardsƒ In this study, several methods of attacking RFID tags were tested, a data map on a ticket was built and the security ranking of the most popular Russian transport cards was created.

Security Analysis of Russian Transport Cards

This paper deals with the problem of student identification by the teacher in the conditions of distance learning. The purpose of this research is to find a way to automatically identify a student by voice using neural networks for sound processing. The research includes two stages: neural network selection and training with Russian dataset, creating an application for teacher.

Anton A. Horoshiy

Papers

Detection of Fake and Provokative Comments in Social Network Using Machine Learning

Security Analysis of Russian Transport Cards

Student Voice Identification Method