scholarly journals Believing and Sharing Information by Fake Sources: An Experiment

2019 ◽  
Author(s):  
Paul Cornelius Bauer ◽  
Bernhard Clemm von Hohenberg
Keyword(s):  
Machine Learning ◽  
21St Century ◽  
Online News ◽  
The Internet ◽  
Survey Experiment ◽  
Fake News ◽  
Mainstream Media ◽  
Digital Era ◽  
Heterogeneity Effects ◽  
Different Levels

The increasing spread of false stories (“fake news”) represents one of the great challenges societies face in the 21st century. A little understood aspect of this phenomenon, and the processing of online news in general, is how sources influence whether people believe and share what they read. In contrast to the pre-digital era, the Internet makes it easy for anyone to imitate well-known and credible sources in name and appearance. In a pre-registered survey experiment, we first investigate the effect of this contrast (real vs. fake source) and find that subjects, as expected, have a higher tendency to believe and a somewhat higher propensity to share news by real sources. We then expose subjects to a number of reports manipulated in content (congruent vs. incongruent with individuals' attitudes), which reveals our most crucial finding. As predicted, people are more likely to believe a news report by a source that has previously given them congruent information. However, this only holds if the source is fake. We further use machine learning to uncover treatment heterogeneity. Effects vary most strongly for different levels of trust in the mainstream media, and having voted for the populist right.

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 SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

2019 ◽  
Vol 11 (1) ◽  
pp. 196 ◽  
Author(s):  
Jong Hwan Suh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Text Mining ◽  
Social Problem ◽  
Social Problems ◽  
Online News ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Related Data ◽  
Learning Techniques

In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not paid attention to the social-problem-specific perspectives of such big data, and it is currently unclear how information technologies can use the big data to identify and manage the ongoing social problems. In this context, this paper introduces and focuses on social-problem-specific key noun terms, namely SocialTERMs, which can be used not only to search the Internet for social-problem-related data, but also to monitor the ongoing and future events of social problems. Moreover, to alleviate time-consuming human efforts in identifying the SocialTERMs, this paper designs and examines the SocialTERM-Extractor, which is an automatic approach for identifying the key noun terms of social-problem-related topics, namely SPRTs, in a large number of online news articles and predicting the SocialTERMs among the identified key noun terms. This paper has its novelty as the first trial to identify and predict the SocialTERMs from a large number of online news articles, and it contributes to literature by proposing three types of text-mining-based features, namely temporal weight, sentiment, and complex network structural features, and by comparing the performances of such features with various machine learning techniques including deep learning. Particularly, when applied to a large number of online news articles that had been published in South Korea over a 12-month period and mostly written in Korean, the experimental results showed that Boosting Decision Tree gave the best performances with the full feature sets. They showed that the SocialTERMs can be predicted with high performances by the proposed SocialTERM-Extractor. Eventually, this paper can be beneficial for individuals or organizations who want to explore and use social-problem-related data in a systematical manner for understanding and managing social problems even though they are unfamiliar with ongoing social problems.

scholarly journals Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1289 ◽  
Author(s):  
Sérgio Branco ◽  
André G. Ferreira ◽  
Jorge Cabral
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Complex Systems ◽  
21St Century ◽  
Silk Road ◽  
The Internet ◽  
Learning Models ◽  
Future Directions ◽  
Machine Learning Models ◽  
Constrained Devices

The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.

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.

scholarly journals The Function and Importance of Fact-Checking Organizations in the Era of Fake News

2019 ◽  
Vol 10 (19) ◽  
pp. 140-160
Author(s):  
Recep Ünal ◽  
Alp Şahin Çiçeklioğlu
Keyword(s):  
Social Media ◽  
New Media ◽  
Digital Literacy ◽  
The Internet ◽  
Fake News ◽  
Political Issues ◽  
Digital Era ◽  
Fact Checking ◽  
Filter Bubbles ◽  
Depth Interviews

The recent increase in usage of concepts such as ‘fake news’ or ‘post-truth’ reveals the importance of digital literacy especially on social media. In the digital era, people’s views on different topics are attempted to be manipulated with disinformation and fake news. Fake content is rapidly replacing the reality among new media users. It is stated with concepts such as ‘filter bubbles’ and ‘echo chambers’ that there is a greater tendency for people to be fed with content that is ideologically appropriate to their own views and to believe in fake news in this content. This article analyzes the structure and functioning of fact-checking organizations in the context of preventing propagation of fake news and improving digital literacy. The research is based on content analysis of verification activities of the fact-checking organization Teyit.org, which is a member of International Fact-Checking Network in Turkey, between January 1 and June 31, 2018. By conducting in-depth interviews with the verification team, propagation of fake news on social networks, fact-checking processes and their methods of combating fake news are revealed. Our article found that fake content spreading specifically through the Internet predominantly consists of political issues.

scholarly journals Journalism and Convergence

2019 ◽  
Vol 2 (1) ◽  
pp. 56
Author(s):  
Carolyne M. Lunga
Keyword(s):  
Freedom Of Expression ◽  
Online News ◽  
The Internet ◽  
Negative Consequences ◽  
Fake News ◽  
Positive Side ◽  
The People ◽  
Production And Consumption ◽  
The Impact ◽  
Online News Sites

<p><em>The internet’s influence on the production and consumption of news has brought about revolutionary changes in the field of journalism. The people previously known as the </em><em>“</em><em>audiences</em><em>”</em><em> are now actively involved in creating and disseminating news via online news sites and websites. This increase in players has both positive and negative consequences for democracy. </em><em></em><em></em></p><p><em>This paper provides an overview of the positive and negative changes that have come about due to convergence. Through an observation of what is happening on various online sites and journalists’ everyday experiences, the paper offers an analysis of the impact globally. On a positive side, for example, citizens are engaging in conversations online with journalists and also with each other on various social platforms on issues that matter to them. The internet is applauded for promoting the number of voices online and freedom of expression. On a negative side, citizens bemoan the rise in fake news and disinformation which is harmful for democracy and is discrediting journalism. Journalism is fundamental as it influences society’s worldview. It thus becomes paramount for media houses and society to be more digital literate so as to distinguish between “real” and “fake” news in order to make more informed decisions.</em></p>

Extreme Gradient Boosting for Cyberpropaganda Detection

2021 ◽  
Author(s):  
Jaouhar Fattahi ◽  
Mohamed Mejri ◽  
Marwa Ziadia
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Gradient Boosting ◽  
The Internet ◽  
Bag Of Words ◽  
Fake News ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Extreme Gradient Boosting ◽  
New Phenomena

Propaganda, defamation, abuse, insults, disinformation and fake news are not new phenomena and have been around for several decades. However, with the advent of the Internet and social networks, their magnitude has increased and the damage caused to individuals and corporate entities is becoming increasingly greater, even irreparable. In this paper, we tackle the detection of text-based cyberpropaganda using Machine Learning and NLP techniques. We use the eXtreme Gradient Boosting (XGBoost) algorithm for learning and detection, in tandem with Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) for text vectorization. We highlight the contribution of gradient boosting and regularization mechanisms in the performance of the explored model.

scholarly journals Fake News Data Exploration and Analytics

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2326
Author(s):  
Mazhar Javed Awan ◽  
Awais Yasin ◽  
Haitham Nobanee ◽  
Ahmed Abid Ali ◽  
Zain Shahzad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Random Forest Classifier ◽  
The Internet ◽  
Fake News ◽  
Learning Models ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Machine Learning Models

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

scholarly journals PROPAGANDA TECHNIQUES IN FAKE NEWS PUBLISHED BY SERBIAN MAINSTREAM MEDIA

2021 ◽  
Vol 2 (2) ◽  
pp. 33-49
Author(s):  
Dušan Aleksić ◽  
Ivana Stamenković
Keyword(s):  
Qualitative Analysis ◽  
Mass Communication ◽  
Theoretical Framework ◽  
Communication Process ◽  
The Internet ◽  
Fake News ◽  
Mainstream Media ◽  
Modern Age ◽  
Political Views ◽  
Definition Of

Observing propaganda as an essential part of the mass-communication process, its techniques and characteristics are changing constantly, both verbally and visually, adapting to the new trends. As Philip Taylor noted, propaganda is ‘a deliberate attempt to persuade people to think and behave in a desired way’ which is based on ‘the conscious, methodical and planned decisions to employ techniques of persuasion designed to achieve specific goals that are intended to benefit those organizing the process’ (Taylor, 2013: 6). If we accept a definition of fake news offered by the Cambridge Dictionary which states that those are ‘false stories that appear to be news, spread on the internet or using other media, usually created to influence political views or as a joke’, then the relation between the two terms becomes more prominent, especially in the modern age. In that context, the goal of this paper is to examine which propaganda aspects are dominant and in what way they are implemented into contemporary fake news, published in Serbian mainstream media. The theoretical framework will be based on findings of contemporary research in the domain of propaganda communication. Through the qualitative analysis approach the authors will conduct the research focusing on detecting and analyzing propaganda techniques used in confirmed fake news articles in Serbian mainstream media which were discovered and deconstructed by reliable and certified fact checkers (Raskrinkavanje and Fake news tragač). The unit of the analysis will be a deconstructed text which is labeled as fake news.

scholarly journals Understanding the Strategies of Creating Fake News in Social Media

2020 ◽  
Author(s):  
Dinusha Vatsalan ◽  
Nalin A.G. Arachchilage
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Data Sets ◽  
Fake News ◽  
Threat Model ◽  
Social Media Platforms ◽  
Model Understanding ◽  
Cyber Criminals

Social media giants like Facebook are struggling to keep up with fake news, in the light of the fact that disinformation diffuses at lightning speed. For example, the COVID-19 (i.e. Coronavirus) pandemic is testing the citizens' ability to distinguish real news from falsifying facts (i.e. disinformation). Cyber-criminals take advantage of the inability to cope with fake news diffusion on social media platforms. Fake news, created as a means to manipulate readers to perform various malicious IT activities such as clicking on fraudulent links associated with the fake news/posts. However, no previous study has investigated the strategies used to create fake news on social media. Therefore, we have analysed five data-sets using Machine Learning (ML) that contain online news articles (i.e. both fake and legitimate news) to investigate strategies of creating fake news on social media platforms. Our study findings revealed a threat model understanding strategies of crafting fake news which may highly likely diffuse on social media platforms.

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