Weighted Accuracy Algorithmic Approach In Counteracting Fake News And Disinformation

07/30/2020
by   Kwadwo Osei Bonsu, et al.
0

As the world is becoming more dependent on the internet for information exchange, some overzealous journalists, hackers, bloggers, individuals and organizations tend to abuse the gift of free information environment by polluting it with fake news, disinformation and pretentious content for their own agenda. Hence, there is the need to address the issue of fake news and disinformation with utmost seriousness. This paper proposes a methodology for fake news detection and reporting through a constraint mechanism that utilizes the combined weighted accuracies of four machine learning algorithms.

READ FULL TEXT

page 9

page 10

page 11

page 12

research
11/01/2020

Fake or Real? A Study of Arabic Satirical Fake News

One very common type of fake news is satire which comes in a form of a n...
research
07/15/2023

The science of fake news

Fake news emerged as an apparent global problem during the 2016 U.S. Pre...
research
01/12/2021

Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task

With the ease of access to information, and its rapid dissemination over...
research
02/04/2020

Fake News Detection by means of Uncertainty Weighted Causal Graphs

Society is experimenting changes in information consumption, as new info...
research
08/05/2019

The Myths of Our Time: Fake News

While the purpose of most fake news is misinformation and political prop...
research
01/03/2022

Testing the Robustness of a BiLSTM-based Structural Story Classifier

The growing prevalence of counterfeit stories on the internet has foster...
research
03/17/2020

FakeYou! – A Gamified Approach for Building and Evaluating Resilience Against Fake News

Nowadays fake news are heavily discussed in public and political debates...

Please sign up or login with your details

Forgot password? Click here to reset