Fake news detection within online social media using supervised artificial intelligence algorithms

2020 ◽  
Vol 540 ◽  
pp. 123174 ◽  
Author(s):  
Feyza Altunbey Ozbay ◽  
Bilal Alatas
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Fake News ◽  
Online Social Media

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 Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

scholarly journals Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

2021 ◽  
Vol 16 (6) ◽  
pp. 1-18
Author(s):  
T. V. Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Social Media ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Word Embedding ◽  
Support Vector ◽  
Fake News ◽  
Short Term ◽  
Term Memory ◽  
Online Social Media ◽  
Long Short Term Memory

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

scholarly journals Towards Combating Pandemic-Related Misinformation in Social Media

2021 ◽  
pp. 140-158
Author(s):  
Isa Inuwa-Dutse
Keyword(s):  
Social Media ◽  
Public Discourse ◽  
Preventive Measures ◽  
State Of The Art ◽  
Fake News ◽  
Social Distancing ◽  
The Public ◽  
Toxic Impact ◽  
Online Social Media ◽  
Social Media Platforms

Conventional preventive measures during pandemics include social distancing and lockdown. Such measures in the time of social media brought about a new set of challenges – vulnerability to the toxic impact of online misinformation is high. A case in point is COVID-19. As the virus propagates, so does the associated misinformation and fake news about it leading to an infodemic. Since the outbreak, there has been a surge of studies investigating various aspects of the pandemic. Of interest to this chapter are studies centering on datasets from online social media platforms where the bulk of the public discourse happens. The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets; (2) offering relevant areas to study using the datasets; and (3) demonstrating how relevant datasets, strategies, and state-of-the-art IT tools can be leveraged in managing the pandemic.

scholarly journals Analysis on Fake News Detection Methodologies

2020 ◽  
Vol 9 (1) ◽  
pp. 1572-1575
Keyword(s):  
Social Media ◽  
News Media ◽  
Low Cost ◽  
Past Research ◽  
Online News ◽  
Easy Access ◽  
Accurate Information ◽  
Fake News ◽  
The Real ◽  
Online Social Media

Fake news is a coinage often used to refer to fabricated news that uses eye-catching headlines for increased sales rather than legitimate well-researched news, spread via online social media. Emergence of fake news has been increased with the immense use of online news media and social media. Low cost, easy access and rapid dissemination of information lead people to consume news from social media. Since the spread rate of these contents are faster it becomes difficult to identify the fake news from the accurate information. People can download articles from sites, share the content, re-share from others and by the end of the day the false information has gone far from its original site that it becomes very difficult to compare with the real news. It is a long standing problem that affects the digital social media due to its serious threats of misleading information, it creates an immense impact on the society. Hence the identification of such news are relevant and so certain measures needs to be taken in order to reduce or distinguish between the real and fake news. This paper provides a survey on recent past research papers done on this domain and provides an idea on different techniques on machine learning and deep learning that could help in the identification of fake and real news.

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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