scholarly journals Fake News Analysis and Graph Classification on a COVID-19 Twitter Dataset

2021 ◽  
Author(s):  
Kriti Gupta ◽  
Katerina Potika
Keyword(s):  
Graph Classification ◽  
Fake News ◽  
News Analysis

scholarly journals A Review : Fake News Analysis and Detection

2020 ◽  
pp. 67-69
Author(s):  
Mridula Arvind Halgekar ◽  
Vidya Kulkarni
Keyword(s):  
Random Forest ◽  
Professional Life ◽  
Personal Life ◽  
Fake News ◽  
News Analysis ◽  
The World ◽  
The Difference

With the growing world in terms of technology and population, the growth of technological use by the population has also increased. The technology has become a part of every human being’s life. It is not just a part of his professional life but also a part of his personal life. There are so many things happening in the world that keeps the world changing. To grow along with this growing world, we need to keep ourselves updated. Media plays an important role in keeping the population updated. The world is kept updated irrespective of the location of the population reading the news and the location of the incident occurring. Fake news is the biggest drawback in this process. We believe what we see and what we read as it the only way to keep ourselves updated. So Fake news hampers the population and may result in unexpected incidents. So it is the need of the hour to understand the difference between real and fake news. This project is for fake news analysis and detection. A dataset of news is considered, pre processing is done and then the fake news and real news are predicted using random forest and xgboost algorithms.

scholarly journals FAKE NEWS DETECTION SYSTEM BASED ON DATA SCIENCE

2020 ◽  
pp. 91-95
Author(s):  
Volodymyr Bazylevych ◽  
◽  
Maria Prybytko ◽  
Keyword(s):  
Input Data ◽  
Data Science ◽  
Detection System ◽  
Fake News ◽  
Political Propaganda ◽  
Inaccurate Information ◽  
News Analysis ◽  
Scientific Facts ◽  
Material Input ◽  
Main Material

Urgency of the research. Today, the task of analyzing the veracity of information in the news, which filled all existing channels for obtaining information, is relevant. Its urgency is related to the need to prevent panic by obtaining inaccurate information, debunking pseudo-scientific facts that can threaten people's lives, combating political propaganda and others.Target settingThis article focuses on the concept of developing a system for detecting fake news, analysis of existing systems and their principles of operation, principles of construction of their algorithms and features of their use.Actual scientific researches and issues analysis.Recent open publications, statistics, and corporate reports were reviewed.Uninvestigated parts of general matters defining.File analysis will be performed using three methods / classifiers and without the use of PassiveAgressive classifier. The calculation and derivation of results is performed by constructing error matrices and calculating accuracy.The research objective.The main purpose of the work is to create a system for detecting fake news on the basis of the considered materials and to achieve the highest possible accuracy.Presenting main material. Input data for the study were selected, prepared and analyzed. Data were studied using the meth-ods /classifiers of Logistic Regression, Decision Tree and Random Forest. The accuracy of detecting fake news is calculated.Conclusions.The proposed system allows to classify news as “fake”or “true ”with an accuracy of 98-99%

A Literature Review of NLP Approaches to Fake News Detection and Their Applicability to Romanian-Language News Analysis

Transilvania ◽  
2020 ◽  
pp. 65-71
Author(s):  
Costin Busioc ◽  
Stefan Ruseti ◽  
Mihai Dascalu
Keyword(s):  
Literature Review ◽  
Research Interest ◽  
Future Research ◽  
Political Environment ◽  
Fake News ◽  
Research Groups ◽  
Low Resource ◽  
News Analysis ◽  
The Social

Fighting fake news is a difficult and challenging task. With an increasing impact on the social and political environment, fake news exert an unprecedently dramatic influence on people’s lives. In response to this phenomenon, initiatives addressing automated fake news detection have gained popularity, generating widespread research interest. However, most approaches targeting English and low-resource languages experience problems when devising such solutions. This study focuses on the progress of such investigations, while highlighting existing solutions, challenges, and observations shared by various research groups. In addition, given the limited amount of automated analyses performed on Romanian fake news, we inspect the applicability of the available approaches in the Romanian context, while identifying future research paths.

scholarly journals Fake News Analysis Modeling Using Quote Retweet

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1377 ◽  
Author(s):  
Yonghun Jang ◽  
Chang-Hyeon Park ◽  
Yeong-Seok Seo
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Good Deal ◽  
Classification Model ◽  
Fake News ◽  
News Analysis ◽  
Medical Issues ◽  
The Neural Network ◽  
Performance Results ◽  
Learning Data

Fake news can confuse many people in the area of politics, culture, healthcare, etc. Fake news refers to news containing misleading or fabricated contents that are actually groundless; they are intentionally exaggerated or provide false information. As such, fake news can distort reality and cause social problems, such as self-misdiagnosis of medical issues. Many academic researchers have been collecting data from social and medical media, which are sources of various information flows, and conducting studies to analyse and detect fake news. However, in the case of conventional studies, the features used for analysis are limited, and the consideration for newly added features of social media is lacking. Therefore, this study proposes a fake news analysis modelling method by identifying a variety of features and collecting various data from Twitter, a social media outlet with a good deal of power in terms of spreading information. The method proposed in this study can increase the accuracy of fake news analysis by acquiring more potential information from the Quote Retweet feature added to Twitter in 2015, compared to the more conventional and common Retweet only. Furthermore, fake news was analysed through neural network-based classification modelling by using the preprocessed data and the identified best features in the learning data. In the performance results, using the neural network-based classifier, the classification model that also used Quote Retweet, showed an improvement in performance over the conventional methods, and it was confirmed that the identified best features had a significant impact on increasing the classification accuracy of fake news.

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