scholarly journals Improving Classification Efficiency of Fake News using Semi-Supervised Method

2022 ◽  
Author(s):  
Suben Kumer Saha ◽  
Khandaker Tabin Hasan
Keyword(s):  
Machine Learning ◽  
News Media ◽  
High Volume ◽  
Online News ◽  
Supervised Machine Learning ◽  
Fake News ◽  
Time Data ◽  
Computing Device ◽  
Internet Connection ◽  
Accuracy Difference

Abstract Online News media which is more accessible, cheaper, and faster to consume, is also of questionable quality as there is less moderation. Anybody with a computing device and internet connection can take part in creating, contributing, and spreading news in online portals. Social media has intensified the problem further. Due to the high volume, velocity, and veracity, online news content is beyond traditional moderation, also known as moderation through human experts. So different machine learning method is being tested and used to spot fake news. One of the main challenges for fake-news classification is getting labeled instances for this high volume of real-time data. In this study, we examined how semi-supervised machine learning can help to decrease the need for labeled instances with an acceptable drop of accuracy. The accuracy difference between the supervised classifier and the semi-supervised classifier is around 0.05 while using only five percent of label instances of the supervised classifier. We tested with logistic regression, SVM, and random forest classifier to prove our hypothesis.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals New perspective? Comparing Frame Occurrence in Online and Traditional News Media Reporting on Europe’s “Migration Crisis”

2019 ◽  
Author(s):  
Christian S. Czymara ◽  
Marijn van Klingeren
Keyword(s):  
Machine Learning ◽  
News Media ◽  
Print Media ◽  
Online News ◽  
Online Media ◽  
Media Reporting ◽  
Machine Learning Methods ◽  
Migration Crisis ◽  
New Perspective ◽  
Key Events

News media have shape-shifted over the last decades, with rising online news suppliers and an increase in online news consumption. We examine how reporting on immigration differs between popular German online and print media over three crucial years of the so-called immigration crisis, from 2015 to 2017. We extend knowledge on framing of the crisis by examining a period covering start, peak and the time after the intake of refugees. Moreover, we establish whether online and print reporting differs in terms of both frame occurrence and variability. Crises generally create an opening for the formation of new perspectives and frames. These conditions provide an ideal test to see whether the focus of media reporting differs between online and print sources. We extract the dominant frames in almost 18,500 articles using machine-learning methods. While results indicate that many frames are, on average, more visible in either online or print media, these differences do not appear to follow a systematic logic. Regarding diversity of frame usage, we find that online media are, on average, more dominated by particular frames compared to print and that frame diversity is largely independent of important key events happening during our period of investigation.

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 From the Deeming Rule to JUUL—US News Coverage of Electronic Cigarettes, 2015–2018

2020 ◽  
Vol 22 (10) ◽  
pp. 1816-1822 ◽  
Author(s):  
Olivia A Wackowski ◽  
Jennah M Sontag ◽  
Binu Singh ◽  
Jessica King ◽  
M Jane Lewis ◽  
...  
Keyword(s):  
News Media ◽  
Electronic Cigarettes ◽  
News Coverage ◽  
High Volume ◽  
The United States ◽  
Online News ◽  
Future Research ◽  
The Public ◽  
News Sources ◽  
Over Time

Abstract Introduction News media may influence public perceptions and attitudes about electronic cigarettes (e-cigarettes), which may influence product use and attitudes about their regulation. The purpose of this study is to describe trends in US news coverage of e-cigarettes during a period of evolving regulation, science, and trends in the use of e-cigarettes. Methods We conducted a content analysis of e-cigarette topics and themes covered in US news articles from 2015 to 2018. Online news databases (Access World News, Factiva) were used to obtain US news articles from the top 34 circulating newspapers, four national wire services, and five leading online news sources. Results The number of articles increased by 75.4% between 2015 and 2018 (n = 1609). Most articles focused on policy/regulation (43.5%) as a main topic, followed by health effects (22.3%) and prevalence/trends (17.9%). Discussion about flavor bans quadrupled (6.1% to 24.6%) and discussion of youth e-cigarette use was most prevalent (58.4%) in 2018, coinciding with an increase in coverage about JUUL. JUUL was mentioned in 50.8% of 2018 articles. Across years, articles more frequently mentioned e-cigarette risks (70%) than potential benefits (37.3%). Conclusions E-cigarettes continue to be a newsworthy topic, with coverage both reflecting numerous changes and events over time, and providing repeated opportunities for informing the public and policymakers about these novel products. Future research should continue to track how discourse changes over time and assess its potential influence on e-cigarette perceptions and policy changes. Implications E-cigarette news coverage in the United States increased between 2015 and 2018 and predominantly focused on policy and regulation. Notable spikes in volume were associated with some but not all major e-cigarette events, including the FDA’s deeming rule, Surgeon General’s report, and release of the National Youth Tobacco Survey data in 2018. Coverage of the 2018 National Academy of Medicine, Engineering, and Sciences report on the Public Health Consequences of E-cigarettes received minimal news coverage. The high volume in 2018 was driven in large part by coverage of the e-cigarette brand JUUL; over half of news articles in 2018 referenced JUUL specifically.

scholarly journals Fake News Detection with Machine Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 124-127
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online News ◽  
The Internet ◽  
Daily Routine ◽  
Fake News ◽  
Criminal Records ◽  
The Public ◽  
Processing Techniques

As the internet is becoming part of our daily routine there is sudden growth and popularity of online news reading. This news can become a major issue to the public and government bodies (especially politically) if its fake hence authentication is necessary. It is essential to flag the fake news before it goes viral and misleads the society. In this paper, various Natural Language Processing techniques along with the number of classifiers are used to identify news content for its credibility.Further this technique can be used for various applications like plagiarismcheck , checking for criminal records.

scholarly journals Modeling of Supervised Machine Learning using Mechanism of Quantum Computing.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012023
Author(s):  
Mukta Nivelkar ◽  
S. G. Bhirud
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
High Volume ◽  
Supervised Machine Learning ◽  
Volume Data ◽  
Quantum Mechanism ◽  
Quantum Machine Learning ◽  
Classical Models ◽  
Quantum Technology ◽  
Classical Computer

Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

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