Early Detection of Fake News from Social Media Networks Using Computational Intelligence Approaches

2021 ◽  
pp. 71-89
Author(s):  
Roseline Oluwaseun Ogundokun ◽  
Micheal Olaolu Arowolo ◽  
Sanjay Misra ◽  
Idowu Dauda Oladipo
Keyword(s):  
Social Media ◽  
Early Detection ◽  
Computational Intelligence ◽  
Fake News ◽  
Social Media Networks

scholarly journals Early detection of cyberbullying on social media networks

2021 ◽  
Vol 118 ◽  
pp. 219-229
Author(s):  
Manuel F. López-Vizcaíno ◽  
Francisco J. Nóvoa ◽  
Victor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Social Media ◽  
Early Detection ◽  
Social Media Networks

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

scholarly journals Neural User Response Generator: Fake News Detection with Collective User Intelligence

2018 ◽  
Author(s):  
Feng Qian ◽  
Chengyue Gong ◽  
Karishma Sharma ◽  
Yan Liu
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Semantic Information ◽  
Experimental Results ◽  
News Article ◽  
Fake News ◽  
Additional Information ◽  
Word Level

Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.

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.

scholarly journals Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection

2020 ◽  
Vol 17 (3) ◽  
pp. 1066 ◽  
Author(s):  
Pablo Lara-Navarra ◽  
Hervé Falciani ◽  
Enrique A. Sánchez-Pérez ◽  
Antonia Ferrer-Sapena
Keyword(s):  
Social Media ◽  
Open Data ◽  
The Internet ◽  
Web Pages ◽  
Fake News ◽  
Social Media Networks ◽  
Analytical Scheme ◽  
False News ◽  
The Right ◽  
Methodological Guidelines

Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool—a database implemented with Neo4j—and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health.

A Deep Learning Model for Early Detection of Fake News on Social Media*

2020 ◽  
Author(s):  
Pakindessama M. Konkobo ◽  
Rui Zhang ◽  
Siyuan Huang ◽  
Toussida T. Minoungou ◽  
Jose A. Ouedraogo ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Early Detection ◽  
Learning Model ◽  
Fake News ◽  
Deep Learning Model

scholarly journals Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Nida Aslam ◽  
Irfan Ullah Khan ◽  
Farah Salem Alotaibi ◽  
Lama Abdulaziz Aldaej ◽  
Asma Khaled Aldubaikil
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Learning Model ◽  
Experimental Results ◽  
Ensemble Model ◽  
Fake News ◽  
Learning Models ◽  
Social Media Networks ◽  
Deep Learning Model

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

scholarly journals Social Media and Fake News in the 2016 Election

2017 ◽  
Vol 31 (2) ◽  
pp. 211-236 ◽  
Author(s):  
Hunt Allcott ◽  
Matthew Gentzkow
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Fake News ◽  
News Stories ◽  
Social Media Networks ◽  
Data Archives ◽  
American Adult ◽  
2016 Election ◽  
False News ◽  
Election News

Following the 2016 US presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: 1) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; 2) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; 3) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and 4) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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