scholarly journals Artificial Intelligence and New Level of Fake News

2020 ◽  
Vol 879 ◽  
pp. 012006
Author(s):  
S Nazar ◽  
M R Bustam
Keyword(s):  
Artificial Intelligence ◽  
Fake News

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 1-12
Author(s):  
Sumit Das ◽  
Manas Kumar Sanyal ◽  
Sarbajyoti Mallik
Keyword(s):  
Artificial Intelligence ◽  
Web Application ◽  
Short Term Memory ◽  
Real Data ◽  
Fake News ◽  
Artificial Intelligence Technique ◽  
News Reports ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

scholarly journals New explainability method for BERT-based model in fake news detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mateusz Szczepański ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Michał Choraś
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
High Performance ◽  
Real Life ◽  
Contemporary Society ◽  
Fake News ◽  
Interpretable Model ◽  
Deep Integration ◽  
The Impact

AbstractThe ubiquity of social media and their deep integration in the contemporary society has granted new ways to interact, exchange information, form groups, or earn money—all on a scale never seen before. Those possibilities paired with the widespread popularity contribute to the level of impact that social media display. Unfortunately, the benefits brought by them come at a cost. Social Media can be employed by various entities to spread disinformation—so called ‘Fake News’, either to make a profit or influence the behaviour of the society. To reduce the impact and spread of Fake News, a diverse array of countermeasures were devised. These include linguistic-based approaches, which often utilise Natural Language Processing (NLP) and Deep Learning (DL). However, as the latest advancements in the Artificial Intelligence (AI) domain show, the model’s high performance is no longer enough. The explainability of the system’s decision is equally crucial in real-life scenarios. Therefore, the objective of this paper is to present a novel explainability approach in BERT-based fake news detectors. This approach does not require extensive changes to the system and can be attached as an extension for operating detectors. For this purposes, two Explainable Artificial Intelligence (xAI) techniques, Local Interpretable Model-Agnostic Explanations (LIME) and Anchors, will be used and evaluated on fake news data, i.e., short pieces of text forming tweets or headlines. This focus of this paper is on the explainability approach for fake news detectors, as the detectors themselves were part of previous works of the authors.

scholarly journals Don't Trust Your Eyes: Image Manipulation in the Age of DeepFakes

2021 ◽  
Vol 6 ◽  
Author(s):  
Johannes Langguth ◽  
Konstantin Pogorelov ◽  
Stefan Brenner ◽  
Petra Filkuková ◽  
Daniel Thilo Schroeder
Keyword(s):  
Artificial Intelligence ◽  
Image Manipulation ◽  
The Political ◽  
Fake News ◽  
Novel Technology ◽  
Political Impact ◽  
The Future ◽  
Recent Developments ◽  
Video Material

We review the phenomenon of deepfakes, a novel technology enabling inexpensive manipulation of video material through the use of artificial intelligence, in the context of today’s wider discussion on fake news. We discuss the foundation as well as recent developments of the technology, as well as the differences from earlier manipulation techniques and investigate technical countermeasures. While the threat of deepfake videos with substantial political impact has been widely discussed in recent years, so far, the political impact of the technology has been limited. We investigate reasons for this and extrapolate the types of deepfake videos we are likely to see in the future.

scholarly journals Geospatial data and artificial intelligence technologies as innovative communication tools for quality education and lifelong learning

2020 ◽  
Vol 7 (1-2) ◽  
pp. 50-71
Author(s):  
Chidinma Henrietta Onwubere
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Lifelong Learning ◽  
Current Trend ◽  
Learning Curves ◽  
Geospatial Data ◽  
Quality Education ◽  
Fake News ◽  
Communication Education ◽  
Time Space

The uniqueness of open and distance learning (ODL) lies in its wide reach to a large audience simultaneously in different locations. No better system than geospatial data and artificial intelligence technologies (GDAITs) can achieve this. Globally, the current trend is to use GDAITs to improve the quality of life and productivity. Education is important for any country’s economy as it enhances the overall life expectancy. Application of GDAITs in educational sector, through broadcast digitization, publishing technologies will record greater achievements in the standard of learning and the literacy of populations. At certain ages in life, people develop apathy towards learning, thus, they are cut off from additional education that could provide them with lifelong learning. With GDAITs, they can be reached with quality education anywhere. Students have constraints of time, space, and finance, for acquisition of study materials. GDAITs are able to create and deploy seamless applications which can collapse these constraints and improve the learning curves of learners. This study investigates the exposure of youths to GDAITs and the influence on their learning patterns. Gerbner’s cultivation theory serves as the theoretical framework. A survey of 200 undergraduate Nigerian students was conducted, using random sampling technique. Findings show that Nigerian youths are highly exposed to GDAITs. THw paper concludes that GDAITs contribute positively and negatively to development in diverse human activities. However, it is highly effective in fostering communication education and research in Nigeria. It recommended that information and communication technology should be taught at all levels of education, so that Nigerians can develop critical minds to distinguish what GDAITs can and cannot do. Media houses should continue to establish platforms to check fake news emanating from social media. Also, attention needs to be focused on media content to ensure that there are enough programmes that would enhance communication education in Nigeria, without fake news parasitism. Keywords: GDAITs, Communication education, Learning processes, Social media, Digitization

scholarly journals A Reliable Artificial Intelligence Model for False News Detection Made by Public Figures

2021 ◽  
Vol 08 (03) ◽  
pp. 01-08
Author(s):  
Prashant Kumar Shrivastava ◽  
Mayank Sharma ◽  
Megha Kamble ◽  
Vaibhav Gore
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Access To Information ◽  
Fake News ◽  
Social Media Networks ◽  
Public Figures ◽  
False News ◽  
Research Challenge

The quick access to information on social media networks as well as its exponential rise also made it difficult to distinguish among fake information or real information. The fast dissemination by way of sharing has enhanced its falsification exponentially. It is also important for the credibility of social media networks to avoid the spread of fake information. So it is emerging research challenge to automatically check for misstatement of information through its source, content, or publisher and prevent the unauthenticated sources from spreading rumours. This paper demonstrates an artificial intelligence based approach for the identification of the false statements made by social network entities. Two variants of Deep neural networks are being applied to evalues datasets and analyse for fake news presence. The implementation setup produced maximum extent 99% classification accuracy, when dataset is tested for binary (true or false) labeling with multiple epochs.

Employing artificial intelligence techniques as a mechanism for investigation, scrutiny, and detection of fake news and rumors

2021 ◽  
Vol 12 (1) ◽  
pp. 117-117
Author(s):  
Yosra Sobeih ◽  
El Taieb EL Sadek
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Machine Intelligence ◽  
The Internet ◽  
Human Intelligence ◽  
Fake News ◽  
The Media ◽  
Media Work ◽  
False News ◽  
Editorial Processing

Modern communication means have imposed many changes on the media work in the different stages of content production, starting from gathering news, visual and editorial processing, verification and verification of the truthfulness of what was stated in it until its publication, so the changes that were stimulated by modern means and technologies and artificial intelligence tools have affected all stages of news and media production, since the beginning of the emergence of rooms. Smart news that depends on human intelligence and then machine intelligence, which has become forced to keep pace with the development in communication means, which has withdrawn in the various stages of production, and perhaps the most important of which is the process of investigation and scrutiny and the detection of false news and rumors in our current era, which has become the spread of information very quickly through the Internet and websites Social media and various media platforms

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