scholarly journals The Trump Effect: With No Peer Review, How Do We Know What to Really Believe on Social Media?

2017 ◽  
Vol 30 (04) ◽  
pp. 270-276 ◽  
Author(s):  
Justin Brady ◽  
Molly Kelly ◽  
Sharon Stein
Keyword(s):  
Social Media ◽  
General Public ◽  
Fake News ◽  
False Information ◽  
Medical Field ◽  
Rapid Spread ◽  
Poor Differentiation ◽  
Fact Checking ◽  
Editorial Review ◽  
Political Election

AbstractSocial media is a source of news and information for an increasing portion of the general public and physicians. The recent political election was a vivid example of how social media can be used for the rapid spread of “fake news” and that posts on social media are not subject to fact-checking or editorial review. The medical field is susceptible to propagation of misinformation, with poor differentiation between authenticated and erroneous information. Due to the presence of social “bubbles,” surgeons may not be aware of the misinformation that patients are reading, and thus, it may be difficult to counteract the false information that is seen by the general public. Medical professionals may also be prone to unrecognized spread of misinformation and must be diligent to ensure the information they share is accurate.

scholarly journals Fact-checking on Twitter: An analysis of the hashtag #StopBulos

2021 ◽  
Vol 55 (2) ◽  
pp. e1371
Author(s):  
Roberto M. Lobato ◽  
Andrea Velandia Morales ◽  
Ángel Sánchez Rodríguez ◽  
Mar Montoya Lozano ◽  
Efraín García Sánchez
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Main Function ◽  
Virtual Networks ◽  
Fake News ◽  
False Information ◽  
Fact Checking ◽  
And Control ◽  
Social Media Network

The fact-checking is an important tool to improve the quality of the information that circulates in virtual networks. Although there are different fact-checking verification agencies, we also found some more informal strategies such as the use of the hashtag #Stopbulos. Thus, this research aims to characterize the #StopBulos hashtag on Twitter as a way to verify information and control the spread of fake news. The results showed that there was diversity among users and the themes of the tweets that included this hashtag, while the main function was to deny fake news. However, it was found that those who achieved greater dissemination were the users with the largest number of followers and institutional character. The implications of using the #StopBulos hashtag as a tool to identify false information on social networks are discussed. Keywords: fake news, post-truth, post-news, social media, network societies

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals Fake and Real News detection Using Python

2020 ◽  
pp. 423-428
Author(s):  
Fakhra Akhtar ◽  
Faizan Ahmed Khan
Keyword(s):  
Social Media ◽  
Fake News ◽  
False Information ◽  
False Negatives ◽  
Mainstream Media ◽  
Social Media Platforms ◽  
The Masses ◽  
Negative Attributes ◽  
Entire Dataset ◽  
F Measure

<p>In the digital age, fake news has become a well-known phenomenon. The spread of false evidence is often used to confuse mainstream media and political opponents, and can lead to social media wars, hatred arguments and debates.Fake news is blurring the distinction between real and false information, and is often spread on social media resulting in negative views and opinions. Earlier Research describe the fact that false propaganda is used to create false stories on mainstream media in order to cause a revolt and tension among the masses The digital rights foundation DRF report, which builds on the experiences of 152 journalists and activists in Pakistan, presents that more than 88 % of the participants find social media platforms as the worst source for information, with Facebook being the absolute worst. The dataset used in this paper relates to Real and fake news detection. The objective of this paper is to determine the Accuracy , precision , of the entire dataset .The results are visualized in the form of graphs and the analysis was done using python. The results showed the fact that the dataset holds 95% of the accuracy. The number of actual predicted cases were 296. Results of this paper reveals that The accuracy of the model dataset is 95.26 % the precision results 95.79 % whereas recall and F-Measure shows 94.56% and 95.17% accuracy respectively.Whereas in predicted models there are 296 positive attributes , 308 negative attributes 17 false positives and 13 false negatives. This research recommends that authenticity of news should be analysed first instead of drafting an opinion, sharing fake news or false information is considered unethical journalists and news consumers both should act responsibly while sharing any news.</p>

scholarly journals A family of falsehoods: Deception, media hoaxes and fake news

2018 ◽  
Vol 39 (3) ◽  
pp. 350-361 ◽  
Author(s):  
Teri Finneman ◽  
Ryan J. Thomas
Keyword(s):  
Social Media ◽  
Fake News ◽  
Public Officials ◽  
False Information ◽  
Media Environment ◽  
News Organizations ◽  
Information Spread ◽  
History Of

“Fake news” became a concern for journalists in 2017 as news organizations sought to differentiate themselves from false information spread via social media, websites and public officials. This essay examines the history of media hoaxing and fake news to help provide context for the current U.S. media environment. In addition, definitions of the concepts are proposed to provide clarity for researchers and journalists trying to explain these phenomena.

Combating Fake News Online

2020 ◽  
pp. 273-289
Author(s):  
Mehmet Fatih Çömlekçi
Keyword(s):  
Content Analysis ◽  
Digital Literacy ◽  
Digital Tools ◽  
Political Polarization ◽  
Fake News ◽  
Depth Interview ◽  
Rapid Spread ◽  
Data Journalism ◽  
Fact Checking ◽  
Methodological Approaches

In today's post-truth environment, besides the increase in political polarization, the rapid spread of fake news infringes on society. In the struggle with fake news, fact-checking services have begun to play an important role. The aim of this chapter is to highlight how fact-checking services work, what their strategies and limitations are, their interaction with users, and the digital tools they use in such interactions. Thus, the platforms Teyit.org (Confirmation) and Doğruluk Payı (Share of Truth) that operate in Turkey have been chosen as exemplary cases. In the study, the content analysis and the in-depth interview methodological approaches have been used together. As a conclusion, it has been revealed that these aforementioned fact-checking services increase their activities during election times, adopt the principles of political impartiality and economic transparency, use the practices of data journalism, interact with users, and try to create a digital literacy ecosystem as an ultimate goal.

scholarly journals False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information?

2020 ◽  
Author(s):  
Cristina Pulido Rodríguez ◽  
Beatriz Villarejo Carballido ◽  
Gisela Redondo-Sama ◽  
Mengna Guo ◽  
Mimar Ramis ◽  
...  
Keyword(s):  
Social Media ◽  
Evidence Based ◽  
World Economic Forum ◽  
Fake News ◽  
Best Interest ◽  
False Information ◽  
Conspiracy Theories ◽  
Sina Weibo ◽  
World Economic ◽  
False News

Since the Coronavirus health emergency was declared, many are the fake news that have circulated around this topic, including rumours, conspiracy theories and myths. According to the World Economic Forum, fake news is one of the threats in today's societies, since this type of information circulates fast and is often inaccurate and misleading. Moreover, fake-news are far more shared than evidence-based news among social media users and thus, this can potentially lead to decisions that do not consider the individual’s best interest. Drawing from this evidence, the present study aims at comparing the type of Tweets and Sina Weibo posts regarding COVID-19 that contain either false or scientific veracious information. To that end 1923 messages from each social media were retrieved, classified and compared. Results show that there is more false news published and shared on Twitter than in Sina Weibo, at the same time science-based evidence is more shared on Twitter than in Weibo but less than false news. This stresses the need to find effective practices to limit the circulation of false information.

scholarly journals Right to be Wrong: Examining the (Im) possibilities of Regulating Fake News while Preserving the Freedom of Expression in Kenya

2019 ◽  
Vol 4 (1) ◽  
pp. 19-46
Author(s):  
Abdulmalik Sugow
Keyword(s):  
Freedom Of Expression ◽  
Peer Networks ◽  
Fake News ◽  
False Information ◽  
Peer To Peer Networks ◽  
Fact Checking ◽  
Intermediary Liability ◽  
Definition Of ◽  
Source Of Information ◽  
Legislative Measures

With the proliferation of peer-to-peer networks as a source of information, concerns on the accuracy of information shared have been raised, necessitating attempts by governments to regulate fake news. Kenya’s Computer Misuse and Cybercrimes Act, for instance, criminalises the intentional dissemination of false or misleading data. However, such regulation has resulted in a different set of concerns, particularly its potential to bring about undue limitation on the freedom of expression. In appraising the approach taken in Kenya of imposing liability on perpetrators, and that taken in some jurisdictions of imposing intermediary liability, the article posits that similar difficulties are faced in regulating fake news – the freedom of expression could be curtailed. This is fuelled by ambiguity in the definition of ‘fake news’. Consequently, this article seeks to find out if indeed, it is possible to regulate fake news while preserving the freedom of expression in Kenya. Further, the article delves into some of the effects the proliferation of fake news has had on the democratic process in Kenya, thereby requiring regulation. In doing so, it tackles fake news from two general conceptions: fake news as calculated disinformation campaigns by individuals for certain purposes, and fake news as an overarching culture of misinformation that enables the spread of false information. Regarding the former, it finds that legislative measures may prove sufficient. However, the latter requires a combination of non-legislative measures such as collaborative measure, awareness initiatives and fact-checking.

scholarly journals Intentional or inadvertent fake news sharing? Fact-checking warnings and users’ interaction with social media content

2020 ◽  
Author(s):  
Alberto Ardèvol-Abreu ◽  
Patricia Delponti ◽  
Carmen Rodríguez-Wangüemert
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Media Content ◽  
Fake News ◽  
Negative Effects ◽  
News Stories ◽  
Social Media Platforms ◽  
Fact Checking ◽  
False News ◽  
News Sharing

The main social media platforms have been implementing strategies to minimize fake news dissemination. These include identifying, labeling, and penalizing –via news feed ranking algorithms– fake publications. Part of the rationale behind this approach is that the negative effects of fake content arise only when social media users are deceived. Once debunked, fake posts and news stories should therefore become harmless. Unfortunately, the literature shows that the effects of misinformation are more complex and tend to persist and even backfire after correction. Furthermore, we still do not know much about how social media users evaluate content that has been fact-checked and flagged as false. More worryingly, previous findings suggest that some people may intentionally share made up news on social media, although their motivations are not fully explained. To better understand users’ interaction with social media content identified or recognized as false, we analyze qualitative and quantitative data from five focus groups and a sub-national online survey (N = 350). Findings suggest that the label of ‘false news’ plays a role –although not necessarily central– in social media users’ evaluation of the content and their decision (not) to share it. Some participants showed distrust in fact-checkers and lack of knowledge about the fact-checking process. We also found that fake news sharing is a two-dimensional phenomenon that includes intentional and unintentional behaviors. We discuss some of the reasons why some of social media users may choose to distribute fake news content intentionally.

Fake News

2018 ◽  
Author(s):  
Bente Kalsnes
Keyword(s):  
Communication Technologies ◽  
Social Motivation ◽  
Research Literature ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories ◽  
Different Types ◽  
Fact Checking ◽  
Definition Of ◽  
Different Levels

Fake news is not new, but the American presidential election in 2016 placed the phenomenon squarely onto the international agenda. Manipulation, disinformation, falseness, rumors, conspiracy theories—actions and behaviors that are frequently associated with the term—have existed as long as humans have communicated. Nevertheless, new communication technologies have allowed for new ways to produce, distribute, and consume fake news, which makes it harder to differentiate what information to trust. Fake news has typically been studied along four lines: Characterization, creation, circulation, and countering. How to characterize fake news has been a major concern in the research literature, as the definition of the term is disputed. By differentiating between intention and facticity, researchers have attempted to study different types of false information. Creation concerns the production of fake news, often produced with either a financial, political, or social motivation. The circulation of fake news refers to the different ways false information has been disseminated and amplified, often through communication technologies such as social media and search engines. Lastly, countering fake news addresses the multitude of approaches to detect and combat fake news on different levels, from legal, financial, and technical aspects to individuals’ media and information literacy and new fact-checking services.

Analysis of Fact-Checking Platforms

2022 ◽  
pp. 227-248
Author(s):  
And Algül ◽  
Gamze Sinem Kuruoğlu
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Fake News ◽  
The Public ◽  
Fact Checking ◽  
To Receive

Social media has become a platform where fake news is abundant. This issue has shown itself effectively yet again during the COVID-19 pandemic. The WHO has named this information pollution in the COVID-19 period as “infodemic.” Due to the COVID-19 pandemic, access to authentic news has become more important than ever before in 2020. Within this context, verified news on teyit.org and dogrulukpayi.com in 2020 were analyzed. Besides, text analysis was conducted on 161 items of news originating in Twitter, and the most-commonly-used words have been found through analysis. The research suggests that news items on topics related to the agenda, which the public feels a need to be informed about, are more likely to be fake news and that Twitter, used broadly by the public to receive news, is preferred as it is easier and faster to spread fake news.

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