scholarly journals Trends in the diffusion of misinformation on social media

2019 ◽  
Vol 6 (2) ◽  
pp. 205316801984855 ◽  
Author(s):  
Hunt Allcott ◽  
Matthew Gentzkow ◽  
Chuan Yu
Keyword(s):  
Social Media ◽  
Relative Magnitude ◽  
Democratic Institutions ◽  
Fake News ◽  
User Interactions ◽  
News Stories ◽  
News Websites ◽  
Social Media Platforms

In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions to limit the spread of false content. We measure trends in the diffusion of content from 569 fake news websites and 9540 fake news stories on Facebook and Twitter between January 2015 and July 2018. User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60%. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that the relative magnitude of the misinformation problem on Facebook has declined since its peak.

Spiritualism and the Resurgence of Fake News

2020 ◽  
pp. 222-237
Author(s):  
Kristy A. Hesketh
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Fake News ◽  
News Stories ◽  
News Reports ◽  
The World ◽  
Spirit World ◽  
Social Media Platforms ◽  
Media Platform ◽  
False News

This chapter explores the Spiritualist movement and its rapid growth due to the formation of mass media and compares these events with the current rise of fake news in the mass media. The technology of cheaper publications created a media platform that featured stories about Spiritualist mediums and communications with the spirit world. These articles were published in newspapers next to regular news creating a blurred line between real and hoax news stories. Laws were later created to address instances of fraud that occurred in the medium industry. Today, social media platforms provide a similar vessel for the spread of fake news. Online fake news is published alongside legitimate news reports leaving readers unable to differentiate between real and fake articles. Around the world countries are actioning initiatives to address the proliferation of false news to prevent the spread of misinformation. This chapter compares the parallels between these events, how hoaxes and fake news begin and spread, and examines the measures governments are taking to curb the growth of misinformation.

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.

scholarly journals Level of Acceptance of News Stories on Social Media Platforms Among Youth in Nigeria

2021 ◽  
Vol 37 (2) ◽  
pp. 210-225
Author(s):  
Festus Prosper Olise ◽  
Keyword(s):  
Social Media ◽  
Sampling Technique ◽  
Fake News ◽  
Mainstream Media ◽  
News Stories ◽  
Multi Stage ◽  
States Data ◽  
Social Media Platforms ◽  
Media Platform ◽  
And Gender

This study investigates the level of acceptance of news stories on social media platforms among youth in Nigeria following the assumption that the proliferation of news stories on social media promotes the circulation of both factual and fake news. The sample consisted of 600 youth; however, 583 validly participated in the study. The participants were equitably selected in six States in Nigeria that represented the six geo-political zones of the country. The multi-stage sampling technique was employed to evenly select the youth from the major cities/towns in the States. Data generated were analysed and presented through descriptive and inferential statistics using SPSS version 20 software. Results show that the youth accepted entertainment news stories more than any other type of news stories on social media platforms. The majority of the youth considered Twitter as the most acceptable social media platform for receiving news stories in Nigeria. Findings also revealed that despite their love to read news stories on social media platforms, the youth’s level of acceptance of it was low. Furthermore, the age and gender of the youth were found to directly influence their level of acceptance of news stories on social media platforms. The study concluded that the multi-dimensional inter-play that characterised the low level of acceptance of news stories on social media platforms among the youth in Nigeria does not portend ominous signs. Keywords: Acceptance, mainstream media, news stories, social media platforms, youth.

scholarly journals Fake News Detection using Machine Learning

2021 ◽  
Vol 40 ◽  
pp. 03003
Author(s):  
Prasad Kulkarni ◽  
Suyash Karwande ◽  
Rhucha Keskar ◽  
Prashant Kale ◽  
Sumitra Iyer
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Extraction ◽  
Fake News ◽  
Detection Model ◽  
News Websites ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Short Time ◽  
The Given

Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.

scholarly journals Who Shares and Comments on News?: A Cross-National Comparative Analysis of Online and Social Media Participation

2017 ◽  
Vol 3 (4) ◽  
pp. 205630511773575 ◽  
Author(s):  
Antonis Kalogeropoulos ◽  
Samuel Negredo ◽  
Ike Picone ◽  
Rasmus Kleis Nielsen
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Digital Media ◽  
Online News ◽  
Media Technologies ◽  
News Stories ◽  
High Interest ◽  
News Websites ◽  
Social Media Platforms ◽  
Cross National

In this article, we present a cross-national comparative analysis of which online news users in practice engage with the participatory potential for sharing and commenting on news afforded by interactive features in news websites and social media technologies across a strategic sample of six different countries. Based on data from the 2016 Reuters Institute Digital News Report, and controlling for a range of factors, we find that (1) people who use social media for news and a high number of different social media platforms are more likely to also engage more actively with news outside social media by commenting on news sites and sharing news via email, (2) political partisans on both sides are more likely to engage in sharing and commenting particularly on news stories in social media, and (3) people with high interest in hard news are more likely to comment on news on both news sites and social media and share stores via social media (and people with high interest in any kind of news [hard or soft] are more likely to share stories via email). Our analysis suggests that the online environment reinforces some long-standing inequalities in participation while countering other long-standing inequalities. The findings indicate a self-reinforcing positive spiral where the already motivated are more likely in practice to engage with the potential for participation offered by digital media, and a negative spiral where those who are less engaged participate less.

scholarly journals Detecting Fake News Using Social Media Platforms

2021 ◽  
Vol 9 (10) ◽  
pp. 1115-1120
Author(s):  
Prof. B. J. Deokate
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Short Term Memory ◽  
Fake News ◽  
Online Social Media ◽  
News Websites ◽  
Computer Scientists ◽  
Social Media Platforms

Abstract: Fake news detection is an interesting topic for computer scientists and social science. The recent growth of the online social media fake news has great impact to the society. There is a huge information from disparate sources among various users around the world. Social media platforms like Facebook, WhatsApp and Twitter are one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from these platforms is becoming a necessary and challenging task. This project proposes a machine learning method which can identify the credibility of an article that will be extracted from the Uniform Resource Locator (URL) entered by the user on the front end of a website. The project uses the five widely used machine learning methods: Long Short Term Memory (LSTM), Random Forest (random tree), Random Forest (decision tree), Decision Tree and Neural Network to give a response telling the user about the credibility of that news. Our initial definition of reliable and unreliable will rely on the human-curated data http://opensources.co. OpenSources.co has a list of about 20 credible news websites and a list of over 700 fake news websites. The proposed model is working well and defining the correctness of results upto 87.45% of accuracy. Keywords: Data Pre-processing, Fake news datasets, ML algorithms, Prediction.

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.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency

2021 ◽  
pp. 1-41
Author(s):  
Donato VESE
Keyword(s):  
Social Media ◽  
Freedom Of Expression ◽  
Self Regulation ◽  
Public Law ◽  
Fake News ◽  
Human Right ◽  
Social Media Platforms ◽  
Definition Of ◽  
The Right ◽  
Media Censorship

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).

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