scholarly journals Reel-time news: As “fake news” dominates headlines, Index’s global team of experienced journalists offers tips on how to spot falsehoods before you click and share

2017 ◽  
Vol 46 (1) ◽  
pp. 52-65
Author(s):  
Rachael Jolley ◽  
Kaya Genç ◽  
Jemimah Steinfeld ◽  
Duncan Tucker ◽  
Abraham T Zere ◽  
...  
Keyword(s):  
Fake News ◽  
Global Team ◽  
Time News

MYTHYA: Fake News Detector, Real Time News Extractor and Classifier

2020 ◽  
Author(s):  
Ashokkumar Thakur ◽  
Sujit Shinde ◽  
Tejas Patil ◽  
Brijesh Gaud ◽  
Vanita Babanne
Keyword(s):  
Real Time ◽  
Fake News ◽  
Time News

scholarly journals Mutual Influence of Users Credibility and News Spreading in Online Social Networks

2021 ◽  
Vol 13 (5) ◽  
pp. 107
Author(s):  
Vincenza Carchiolo ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni ◽  
Marialaura Previti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Mutual Influence ◽  
Collective Intelligence ◽  
Online Information ◽  
Fake News ◽  
Potential Growth ◽  
Multiplex Network ◽  
Time News ◽  
Information Providers

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

scholarly journals Detecting Fake News using Machine Learning Algorithms

2020 ◽  
Author(s):  
Harika Kudarvalli ◽  
Jinan Fiaidhi
Keyword(s):  
Social Media ◽  
Real Time ◽  
Short Term Memory ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fake News ◽  
Time Data ◽  
Time News ◽  
Social Media Platforms

Spreading fake news has become a serious issue in the current social media world. It is broadcasted with dishonest intentions to mislead people. This has caused many unfortunate incidents in different countries. The most recent one was the latest presidential elections where the voters were mis lead to support a leader. Twitter is one of the most popular social media platforms where users look up for real time news. We extracted real time data on multiple domains through twitter and performed analysis. The dataset was preprocessed and user_verified column played a vital role. Multiple machine algorithms were then performed on the extracted features from preprocessed dataset. Logistic Regression and Support Vector Machine had promising results with both above 92% accuracy. Naive Bayes and Long-Short Term memory didn't achieve desired accuracies. The model can also be applied to images and videos for better detection of fake news.

scholarly journals Detecting Fake News using Machine Learning Algorithms

2020 ◽  
Author(s):  
Harika Kudarvalli ◽  
Jinan Fiaidhi
Keyword(s):  
Social Media ◽  
Real Time ◽  
Short Term Memory ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fake News ◽  
Time Data ◽  
Time News ◽  
Social Media Platforms

Spreading fake news has become a serious issue in the current social media world. It is broadcasted with dishonest intentions to mislead people. This has caused many unfortunate incidents in different countries. The most recent one was the latest presidential elections where the voters were mis lead to support a leader. Twitter is one of the most popular social media platforms where users look up for real time news. We extracted real time data on multiple domains through twitter and performed analysis. The dataset was preprocessed and user_verified column played a vital role. Multiple machine algorithms were then performed on the extracted features from preprocessed dataset. Logistic Regression and Support Vector Machine had promising results with both above 92% accuracy. Naive Bayes and Long-Short Term memory didn't achieve desired accuracies. The model can also be applied to images and videos for better detection of fake news.

Maier-Kolumne – Fake News

physiopraxis ◽  
2018 ◽  
Vol 16 (07/08) ◽  
pp. 58-58
Author(s):  
Ulrike Maier
Keyword(s):  
Fake News

Eine Hausärztin, die, statt ein Physio-Rezept auszustellen, auf Übungen aus der Apotheken Umschau verweist? Unmöglich, sollte man meinen. Doch Physiotherapeutin Ulrike Maier belehrt eines Besseren. Ihre unwissende Hausärztin von nebenan liefert ihren Patienten gerne mal „alternative Fakten“ und hat bei ihren physiotherapeutischen Kenntnissen noch dringend Nachholbedarf.

Datos masivos, algoritmización y nuevos medios frente a desinformación y fake news. Bots para minimizar el impacto en las organizaciones

2020 ◽  
pp. 101-114
Author(s):  
Jesús Miguel Flores Vivar
Keyword(s):  
Fake News

Éste trabajo parte de una información compartida por muchos investigadores. La comprensión de la desinformación como un fenómeno que va mucho más allá del término “noticias falsas”. Estos términos han sido apropiados y usados engañosamente por poderosos actores para desestimar y poner en entredicho la cobertura informativa que ya atraviesa momentos críticos sobre la credibilidad.

The Birth, Growth and Future of Systems Approach

2020 ◽  
Vol 31 (3) ◽  
pp. 113-114
Author(s):  
Nelson Laville ◽  
Kenrick Witty ◽  
Ulises Garcia
Keyword(s):  
United States ◽  
Systems Approach ◽  
Agricultural Research ◽  
United States Department ◽  
Department Of Agriculture ◽  
States Department ◽  
Global Team ◽  
Video Link ◽  
Agricultural Research Service ◽  
Research Service

The Beyond Compliance Global team held an interview by video link with Dr Eric Jang, United States Department of Agriculture, Agricultural Research Service (USDA/ARS), now retired. His lab was based in Hawaii, where he continues to reside. Eric was an early advocate and one of the originators of the concepts for Systems Approach.

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