Assessing online media reliability: assigning a trust metric value and detecting 'Fake News' (Conference Presentation)

2019 ◽  
Author(s):  
Nicholas Snell ◽  
Terry Traylor ◽  
Jeremy Straub
Keyword(s):  
Online Media ◽  
Fake News ◽  
Trust Metric

An Ontological Approach to Detecting Fake News in Online Media

2021 ◽  
Author(s):  
Svitlana Mazepa ◽  
Serhiy Banakh ◽  
Andriy Melnyk ◽  
Sergiy Pugach ◽  
Oleksandra Yavorska ◽  
...  
Keyword(s):  
Online Media ◽  
Fake News ◽  
Ontological Approach

scholarly journals The Dangers of Hoaxes in Building Civil Society in the Era of the Industrial Revolution 4.0

2021 ◽  
pp. 117-138
Author(s):  
Muhamad Basitur Rijal Gus Rijal ◽  
Ahyani Hisam ◽  
Abdul Basit
Keyword(s):  
Civil Society ◽  
Public Space ◽  
Free Speech ◽  
Industrial Revolution ◽  
Online Media ◽  
Fake News ◽  
The Public ◽  
Technological Advances ◽  
The Impact ◽  
The Ideal

Civil society (civil society) as the ideal structure of society's life that is aspired to, but building a civil society is not easy. There are preconditions that must be met by the community in making it happen. Coupled with technological advances in the era of the Industrial Revolution 4.o like today, where information can spread easily through various online media unlimitedly in spreading hoaxes. This research seeks to uncover the dangers of hoaxes in building civil society. This research uses descriptive analytical method by examining the sources of literature related to building civil society in the Industrial Revolution 4.o. This research found that the public space is a means of free speech; democratic behavior; tolerant; pluralism; and social justice can shape civil society. whereas the impact of hoax news greatly affects the way people perceive a certain issue, so that people cannot distinguish which news is real or fake news which causes them to be incited by fake news that is spread.

scholarly journals Exploring Author Profiling for Fake News Detection

2021 ◽  
Author(s):  
Shloak Rathod
Keyword(s):  
False Positive Rate ◽  
True Positive Rate ◽  
Supervised Machine Learning ◽  
Online Media ◽  
Fake News ◽  
Writing Style ◽  
Short Term ◽  
Positive Rate ◽  
Rapid Dissemination ◽  
Author Profiling

<div><div><div><p>The proliferation of online media allows for the rapid dissemination of unmoderated news, unfortunately including fake news. The extensive spread of fake news poses a potent threat to both individuals and society. This paper focuses on designing author profiles to detect authors who are primarily engaged in publishing fake news articles. We build on the hypothesis that authors who write fake news repeatedly write only fake news articles, at least in short-term periods. Fake news authors have a distinct writing style compared to real news authors, who naturally want to maintain trustworthiness. We explore the potential to detect fake news authors by designing authors’ profiles based on writing style, sentiment, and co-authorship patterns. We evaluate our approach using a publicly available dataset with over 5000 authors and 20000 articles. For our evaluation, we build and compare different classes of supervised machine learning models. We find that the K-NN model performed the best, and it could detect authors who are prone to writing fake news with an 83% true positive rate with only a 5% false positive rate.</p></div></div></div>

scholarly journals Online media, fake news, partisanship and the 2019 Nigerian presidential elections

2021 ◽  
Author(s):  
Adeyanju Apejoye
Keyword(s):  
Presidential Elections ◽  
Online Survey ◽  
Online Media ◽  
Fake News

The data analysed in the study were collected through online survey and analysed using simple percentage.

The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016

2017 ◽  
Vol 20 (5) ◽  
pp. 2028-2049 ◽  
Author(s):  
Chris J Vargo ◽  
Lei Guo ◽  
Michelle A Amazeen
Keyword(s):  
Big Data ◽  
News Media ◽  
Agenda Setting ◽  
News Coverage ◽  
Big Data Analysis ◽  
Online Media ◽  
Fake News ◽  
News Websites ◽  
Partisan Media ◽  
Selection Of

This study examines the agenda-setting power of fake news and fact-checkers who fight them through a computational look at the online mediascape from 2014 to 2016. Although our study confirms that content from fake news websites is increasing, these sites do not exert excessive power. Instead, fake news has an intricately entwined relationship with online partisan media, both responding and setting its issue agenda. In 2016, partisan media appeared to be especially susceptible to the agendas of fake news, perhaps due to the election. Emerging news media are also responsive to the agendas of fake news, but to a lesser degree. Fake news coverage itself is diverging and becoming more autonomous topically. While fact-checkers are autonomous in their selection of issues to cover, they were not influential in determining the agenda of news media overall, and their influence appears to be declining, illustrating the difficulties fact-checkers face in disseminating their corrections.

scholarly journals Information Behavior Culture of the RuNet Audience

2019 ◽  
Vol 8 (3) ◽  
pp. 984-988
Keyword(s):  
The Internet ◽  
Online Media ◽  
Fake News ◽  
State Institute ◽  
Consumption Culture ◽  
News Agencies ◽  
The Media ◽  
The Russian Federation ◽  
The Republic ◽  
Source Of Information

The purpose of this study is to analyze the formation of a media consumption culture in the information-rich multiconfessional and bilingual region of the Russian Federation – the Republic of Tatarstan. The authors of this article conducted a survey of 200 respondents aged 19-55 who are active users of the RuNet. The survey was carried out among students of the Kazan State Institute of Culture and Kazan Federal University, as well as media professionals from the Republic of Tatarstan. The anonymous survey was conducted in January-March 2019. Of all the respondents participated in this survey, 56% were aged 19-20. Eighty-three percent of the respondents were female – students, teachers and media workers of the Republic of Tatarstan. Sixty-five percent of them combined their education with work. Ninety-eight percent of the respondents received information from the Internet, 76% watched information programs on television, 27% listened to the radio and only 7.5% of the respondents still read newspapers. Sixty-eight percent of the surveyed trusted messages received from news agencies, while 78% trusted news messages received from news aggregators. Ninety percent of the respondents trusted information received from online media; 11% trusted the information received from social networks and only 4.5% of the respondents trusted the information discussed in blogs. The high percentage of trust to information obtained from the media and the low percentage of trust to information obtained from blogs indicates the current culture of media use and media literacy of the population in the situation with fake news. Of all the respondents answering the question "Do you refer to the source of information you use on the Internet?", 91.5% answered positively. Disturbingly, 92.5% of the surveyed believe that they do not have to pay for the information received from online media. The authors explain the refusal to pay for content with a small amount of exclusive and analytical materials in the information field of the Republic of Tatarstan

scholarly journals Detecting Fake News Tweets from Twitter

2021 ◽  
Vol 23 (08) ◽  
pp. 532-537
Author(s):  
Cherlakola Abhinav Reddy ◽  
◽  
Sai Nitesh Gadiraju ◽  
Dr. Samala Nagaraj ◽  
◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Online Media ◽  
Fake News ◽  
Breaking News ◽  
Learning Techniques ◽  
Vector Machines

Online media has progressively obtained integral to the route billions of individuals experience news and occasions, frequently bypassing writers—the conventional guardians of breaking news. Occasions,in reality, make a relating spike of posts (tweets) on Twitter. This projects a great deal of significance on the validity of data found via online media stages like Twitter. We have utilized different managed learning techniques like Naïve Bayes, Decision Trees, and Support Vector Machines on the information to separate tweets among genuine and counterfeit news. For our AI models, we have utilized tweet and client highlights as our indicators. We accomplished a precision of 88% utilizing the Random Forest classifier and 88% utilizing the Decision tree. Notwithstanding, we accept that breaking down client records would build the accuracy of our models.

scholarly journals Fake News Detection using RNN-LSTM

2021 ◽  
Vol 9 (10) ◽  
pp. 1731-1737
Author(s):  
Samarth Mengji
Keyword(s):  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Principal Factor ◽  
Online Media ◽  
Fake News ◽  
Short Term ◽  
Web Based ◽  
Personal Level ◽  
Long Short Term Memory

Abstract: Fake news distribution is a social phenomenon that can't be avoided on a personal level or through web-based social media like Facebook and Twitter. We're interested in counterfeit news because it's one of many sorts of double dealing in online media, but it's a more severe one because it's designed to deceive people. We're concerned about this now that we've seen what's going on. We are concerned about this issue because we have seen how, through the usage of social correspondence, this marvel has recently caused a shift in the direction of society and people groupings, as well as their opinions. Along these lines, we chose to confront and decrease this wonder, which is as yet the principal factor to pick a large portion of our choices. Our objective in this study is to develop a detector that can predict if a piece of news is false based just on its content, and then attack the problem using RNN method models LSTMs and Bi-LSTMs to tackle the problem from a basic deep learning viewpoint. Keywords: RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), Fake news detection, Deep learning

scholarly journals INTERTEXTUALITY, HYPERTEXTUALITY AND MULTIMODALITY IN THE MEDIA ON THE TOPIC COVID-19

2020 ◽  
pp. 8-22
Author(s):  
Ivanka MAVRODIEVA
Keyword(s):  
Social Networks ◽  
Public Relations ◽  
Print Media ◽  
Electronic Media ◽  
Online Media ◽  
Fake News ◽  
Survey Period ◽  
The Media

Thе article presents the results of a study based on the news surrounding COVID-19 in electronic media sites, print media and online media in Bulgaria through the prism of three notions: intertextuality, hypertextuality and multimodality. The survey period covers three months: February – April 2020 (the beginning of wider dissemination of information about COVID-19 in the media until the establishment of a peak of patients, hospitalized and carriers of the virus). A discursive, media and communicative analysis of a corpus has been conducted and then divided into four sub-corpora, which include online publications, videos, memes, photos, infographics and more. The analysis focuses on online publications in order to pinpoint the manifestations of intertextuality, mainly on a verbal level; the external and internal hyper textuality and the role of hyperlinks are investigated too. The establishment of multimodality in official media information and memes reflecting situations related to the coronavirus crisis (COVID-19) are examined in part three. Linguistic and communication features are also presented in terms of metadiscourse and intervisuality, which are carried out in the events of institutional public relations. The article also presents groups of terms and expressions used in expert and statemen’s statements in media sites, online media and social networks to present the information about overcoming the coronavirus crisis and preventing the spread of fake news and fake content.

scholarly journals Transfer learning and GRU-CRF augmentation for COVID-19 fake news detection

2021 ◽  
pp. 53-53
Author(s):  
Andrea Karnyoto ◽  
Chengjie Sun ◽  
Bingquan Liu ◽  
Xiaolong Wang
Keyword(s):  
Political Parties ◽  
Transfer Learning ◽  
Hybrid Model ◽  
The Internet ◽  
Online Media ◽  
Fake News ◽  
Baseline Model

The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset. This research proved that our hybrid model with augmentation got better accuracy compared to our baseline model. It also showed that BERT gave a better result than GPT2 in all models; the highest accuracy we achieved for BERT is 0.9196, and GPT2 is 0.8986.

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