Aletheia: A Fake News Detection System for Hindi

2022 ◽  
Author(s):  
Jathin Badam ◽  
Akash Bonagiri ◽  
Kvln Raju ◽  
Dipanjan Chakraborty
Keyword(s):  
Detection System ◽  
Fake News

Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

scholarly journals Automated Fake News Detection in the Age of Digital Libraries

2020 ◽  
Vol 39 (4) ◽  
Author(s):  
Uğur Mertoğlu ◽  
Burkay Genç
Keyword(s):  
Digital Media ◽  
Digital Libraries ◽  
Detection System ◽  
Digital Content ◽  
Fake News ◽  
News Content ◽  
Production And Consumption ◽  
Digital News ◽  
Rapid Dissemination ◽  
Use Of Social Media

The transformation of printed media into digital environment and the extensive use of social media have changed the concept of media literacy and people’s habit of consuming news. While this faster, easier, and comparatively cheaper opportunity offers convenience in terms of people's access to information, it comes with a certain significant problem: Fake News. Due to the free production and consumption of large amounts of data, fact-checking systems powered by human efforts are not enough to question the credibility of the information provided, or to prevent its rapid dissemination like a virus. Libraries, known as sources of trusted information for ages, are facing with the problem because of this difficulty. Considering that libraries are undergoing digitisation processes all over the world and providing digital media to their users, it is very likely that unchecked digital content will be served by world’s libraries. The solution is to develop automated mechanisms that can check the credibility of digital content served in libraries without manual validation. For this purpose, we developed an automated fake news detection system based on the Turkish digital news content. Our approach can be modified for any other language if there is labelled training material. The developed model can be integrated into libraries’ digital systems to label served news content as potentially fake whenever necessary, preventing uncontrolled falsehood dissemination via libraries.

scholarly journals Building a Dataset for Detecting Fake News in Amharic Language

2021 ◽  
pp. 76-83
Author(s):  
Tewodros Tazeze ◽  
Raghavendra R
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Detection System ◽  
Fake News ◽  
News Sources ◽  
Vernacular Language ◽  
Semitic Language ◽  
Media Platform ◽  
Tremendous Challenge ◽  
High Range

The rapid growth and expansion of social media platform has filled the gap of information exchange in the day to day life. Apparently, social media is the main arena for disseminating manipulated information in a high range and exponential rate. The fabrication of twisted information is not limited to ones language, society and domain, this is particularly observed in the ongoing COVID-19 pandemic situation. The creation and propagation of fabricated news creates an urgent demand for automatically classification and detecting such distorted news articles. Manually detecting fake news is a laborious and tiresome task and the dearth of annotated fake news dataset to automate fake news detection system is still a tremendous challenge for low-resourced Amharic language (after Arabic, the second largely spoken Semitic language group). In this study, Amharic fake news dataset are crafted from verified news sources and various social media pages and six different machine learning classifiers Naïve bays, SVM, Logistic Regression, SGD, Random Forest and Passive aggressive Classifier model are built. The experimental results show that Naïve bays and Passive Aggressive Classifier surpass the remaining models with accuracy above 96% and F1- score of 99%. The study has a significant contribution to turn down the rate of disinformation in vernacular language.

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%

scholarly journals Fakebuster: Fake News Detection System using Logistic Regression Technique In Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2407-2410
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Web Application ◽  
Detection System ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Fake News ◽  
Potential Threat ◽  
News Content ◽  
Detection Model

The uncontrollable spread of fake news through the net is irresistible in this globalization era. Fake news dissemination cannot be tolerated as the bad impacts of it to the society is really worrying. Furthermore, this will lead to more significant problems and potential threat such as confusion, misconceptions, slandering and luring users to share provocative lies made from fabricated news through their social media to occur. Within Malaysia context, there is lack in platform for fake news detection in Malay language articles and most of Malaysians received news through their social messaging applications. Fake news can be certainly solved by the aid of artificial intelligence which includes machine learning algorithms. The objective of this project is to propose a fake news detection model using Logistic Regression, to evaluate the performance of Logistic Regression as fake news detection model and to develop a web application that allows entry of a news content or news URL. In this study, Logistic Regression was applied in detecting fake news. Model development methodology is referenced and followed in this project. Based on existing studies, Logistic Regression showed a good performance in classification task. In addition, stancedetection approach is added to improve the accuracy of the model performance. Based on analysis made, this model within stance detection approach yields an excellent accuracy using TF-IDF feature in constructing this fake news model. This model is then integrated with web service that accepts input either news URL or news content in text which is then checked for its truth level through “FAKEBUSTER” application.

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