Cracking Open the News Feed

2021 ◽  
Vol 1 ◽  
Author(s):  
Andy Guess ◽  
Kevin Aslett ◽  
Joshua Tucker ◽  
Richard Bonneau ◽  
Jonathan Nagler
Keyword(s):  
Social Media ◽  
Fake News ◽  
Older Americans ◽  
News Sources ◽  
Older Users ◽  
Different Types ◽  
Supervised Classifiers ◽  
Sharing Patterns ◽  
First Time

In this study, we analyze for the first time newly available engagement data covering millions of web links shared on Facebook to describe how and by which categories of U.S. users different types of news are seen and shared on the platform. We focus on articles from low-credibility news publishers, credible news sources, purveyors of clickbait, and news specifically about politics, which we identify through a combination of curated lists and supervised classifiers. Our results support recent findings that more fake news is shared by older users and conservatives and that both viewing and sharing patterns suggest a preference for ideologically congenial misinformation. We also find that fake news articles related to politics are more popular among older Americans than other types, while the youngest users share relatively more articles with clickbait headlines. Across the platform, however, articles from credible news sources are shared over 5 times more often and viewed over 7 times more often than articles from low-credibility sources. These findings offer important context for researchers studying the spread and consumption of information — including misinformation — on social media.

Fake It “Till You Make It”: Debunking Fake News in a Post-Truth America

2019 ◽  
Vol 8 (1) ◽  
pp. 114-133
Keyword(s):  
Social Media ◽  
First Amendment ◽  
Presidential Election ◽  
Political Discourse ◽  
Cutting Edge ◽  
American People ◽  
Fake News ◽  
News Sources ◽  
The Media ◽  
The U.S

Since the 2016 U.S. presidential election, attacks on the media have been relentless. “Fake news” has become a household term, and repeated attempts to break the trust between reporters and the American people have threatened the validity of the First Amendment to the U.S. Constitution. In this article, the authors trace the development of fake news and its impact on contemporary political discourse. They also outline cutting-edge pedagogies designed to assist students in critically evaluating the veracity of various news sources and social media sites.

scholarly journals Leadership in a post-truth era: A new narrative disorder?

Leadership ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 135-151 ◽  
Author(s):  
Hamid Foroughi ◽  
Yiannis Gabriel ◽  
Marianna Fotaki
Keyword(s):  
Social Media ◽  
Economic Crisis ◽  
Fake News ◽  
Special Issue ◽  
Conspiracy Theories ◽  
Digital Revolution ◽  
Counter Narrative ◽  
Counter Narratives ◽  
Different Types ◽  
Historical Periods

This essay, and the special issue it introduces, seeks to explore leadership in a post-truth age, focusing in particular on the types of narratives and counter-narratives that characterize it and at times dominate it. We first examine the factors that are often held responsible for the rise of post-truth in politics, including the rise of relativist and postmodernist ideas, dishonest leaders and bullshit artists, the digital revolution and social media, the 2008 economic crisis and collapse of public trust. We develop the idea that different historical periods are characterized by specific narrative ecologies, which, by analogy to natural ecologies, can be viewed as spaces where different types of narrative and counter-narrative emerge, interact, compete, adapt, develop and die. We single out some of the dominant narrative types that characterize post-truth narrative ecologies and highlight the ability of language to ‘do things with words’ that support both the production of ‘fake news’ and a type of narcissistic leadership that thrive in these narrative ecologies. We then examine more widely leadership in post-truth politics focusing on the resurgence of populist and demagogical types along with the narratives that have made these types highly effective in our times. These include nostalgic narratives idealizing a fictional past and conspiracy theories aimed at arousing fears about a dangerous future.

scholarly journals The uncertain first-time voter: Effects of political media exposure on young citizens’ formation of vote choice in a digital media environment

2017 ◽  
Vol 20 (9) ◽  
pp. 3243-3265 ◽  
Author(s):  
Jakob Ohme ◽  
Claes H. de Vreese ◽  
Erik Albaek
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Panel Study ◽  
Media Exposure ◽  
Political Information ◽  
Vote Choice ◽  
Election Campaign ◽  
Media Environment ◽  
News Sources ◽  
First Time

The digital media environment changes the way citizens receive political information, also during an election campaign. Particularly first-time voters increasingly use social media platforms as news sources. Yet, it is less clear how accessing political information in such a unique social setting affects these cohorts’ decision-making processes during an election campaign, compared to experienced voters. We compare effects of these two groups’ political information exposure on their vote choice certainty during the 2015 Danish national election. We furthermore test how the relation between exposure and certainty can be mediated by active campaign participation. An 11-wave national panel study was conducted, using a smartphone-based assessment of citizens’ ( n = 1108) media exposure and vote choice certainty across the campaign period. Results suggest that first-time voters’ social media exposure is responsible for their increase in certainty as the campaign progresses, while this effect is absent for experienced voters.

Potential of virtual-social-physical convergence for project manufacturing

2014 ◽  
Vol 25 (8) ◽  
pp. 1209-1223 ◽  
Author(s):  
Stephen Fox
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Design Methodology ◽  
Digital Data ◽  
Manufacturing Companies ◽  
Virtual Design ◽  
Content Type ◽  
Different Types ◽  
First Time ◽  
Practical Implications

Purpose – The purpose of this paper is to provide an analysis of how virtual-social-physical (VSP) convergence can affect different types of project manufacturing. In particular, VSP convergence that involves combining the read-write functionality of Web 2.0 and related social media together with digital tools for virtual design and for physical manufacturing. Design/methodology/approach – Literature review and interviews with experts in technologies covering VSP convergence: digital data capture, photogrammetry, generative computation, Web 2.0 and social media, digitally driven manufacturing. Findings – VSP convergence can enable the replacement of slow and expensive traditional project manufacturing practices with much faster and less expensive digitally driven technologies. Practical implications – There are new opportunities for expansion of some types of project manufacturing. Notably, there are opportunities in non-industrial developing countries because VSP convergence reduces reliance on industrial infrastructure for the manufacturing of goods. By contrast, opportunities may be limited for expansion of established project manufacturing companies with exclusive brands. Originality/value – The originality is that VSP convergence is related to different types of project manufacturing. Based on VSP convergence, traditional types and new types of project manufacturing are categorized together for the first time. The value of this paper is that it is explained how VSP convergence can address barriers to expansion of different types of project manufacturing.

scholarly journals Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

scholarly journals Building a Dataset for Detecting Fake News in Amharic Language

2021 ◽  
pp. 76-83
Author(s):  
Tewodros Tazeze ◽  
Raghavendra R
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Detection System ◽  
Fake News ◽  
News Sources ◽  
Vernacular Language ◽  
Semitic Language ◽  
Media Platform ◽  
Tremendous Challenge ◽  
High Range

The rapid growth and expansion of social media platform has filled the gap of information exchange in the day to day life. Apparently, social media is the main arena for disseminating manipulated information in a high range and exponential rate. The fabrication of twisted information is not limited to ones language, society and domain, this is particularly observed in the ongoing COVID-19 pandemic situation. The creation and propagation of fabricated news creates an urgent demand for automatically classification and detecting such distorted news articles. Manually detecting fake news is a laborious and tiresome task and the dearth of annotated fake news dataset to automate fake news detection system is still a tremendous challenge for low-resourced Amharic language (after Arabic, the second largely spoken Semitic language group). In this study, Amharic fake news dataset are crafted from verified news sources and various social media pages and six different machine learning classifiers Naïve bays, SVM, Logistic Regression, SGD, Random Forest and Passive aggressive Classifier model are built. The experimental results show that Naïve bays and Passive Aggressive Classifier surpass the remaining models with accuracy above 96% and F1- score of 99%. The study has a significant contribution to turn down the rate of disinformation in vernacular language.

scholarly journals Will the crowd game the algorithm? Using layperson judgments to combat misinformation on social media by downranking distrusted sources

2019 ◽  
Author(s):  
Ziv Epstein ◽  
Gordon Pennycook ◽  
David Gertler Rand
Keyword(s):  
Social Media ◽  
Political Polarization ◽  
Survey Experiment ◽  
Fake News ◽  
News Sources ◽  
Ranking Algorithms ◽  
Social Media Platforms ◽  
Highly Correlated ◽  
High Level ◽  
Survey Responses

How can social media platforms fight the spread of misinformation? One possibility is to use newsfeed algorithms to downrank content from sources that users rate as untrustworthy. But will laypeople unable to identify misinformation sites due to motivated reasoning or lack of expertise? And will they “game” this crowdsourcing mechanism to promote content that aligns with their partisan agendas? We conducted a survey experiment in which N = 984 Americans indicated their trust in numerous news sites. Half of the participants were told that their survey responses would inform social media ranking algorithms - creating a potential incentive to misrepresent their beliefs. Participants trusted mainstream sources much more than hyper-partisan or fake news sources, and their ratings were highly correlated with professional fact-checker judgments. Critically, informing participants that their responses would influence ranking algorithms did not diminish this high level of discernment, despite slightly increasing the political polarization of trust ratings.

The effect of news consumption on fake news efficacy

2020 ◽  
Author(s):  
Sylvia Chan-Olmsted ◽  
Yufan Sunny Qin
Keyword(s):  
Social Media ◽  
Self Efficacy ◽  
Online Survey ◽  
Fake News ◽  
Mainstream Media ◽  
News Consumption ◽  
News Sources ◽  
News Source ◽  
Perceived Effects ◽  
Use Of Social Media

The increasing use of social media has led to the growing reliance of social media as a news source. The viral nature of social platforms inevitably elevates the viral impact of fake news. As both academia and practitioners touted media literacy as a means of combating fake news or misinformation, little is known about the nature of relevant efficacies. Existent literature points to the plausible contribution of media consumption, self-efficacy of fake news and perceived impact of fake news in this process. Therefore, this study explored the relationship between consumers’ news consumption, such as fake news experiences/perceptions, news sources and news consumption motives; and fake news perceptions like self-efficacy and impacts. This study conducted an online survey to examine the proposed hypotheses and research questions. The findings suggest that consumers’ previous experiences and consumption motives are connected with their perceived effects and efficacy of fake news. In addition, different news sources (i.e. mainstream media and social media) exert diverse effects on fake news self-efficacy.

scholarly journals Fake News Detection on Social Media

2020 ◽  
pp. 1132-1137
Author(s):  
Dipti Chaudhari ◽  
Krina Rana ◽  
Radhika Tannu ◽  
Snehal Yadav
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Smart Phone ◽  
Online News ◽  
Support Vector ◽  
Fake News ◽  
News Sources ◽  
Application Architecture ◽  
News Websites ◽  
Facebook Pages

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and the articles which are circulated among the social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs and social networking sites. It can be considered that social media has replaced the traditional media and become one of the main platforms for spreading news. News on social media trends to travel faster and easier than traditional news sources due to the internet accessibility and convenience. It is harmful for the society to believe on the rumors and pretend to be a news. The basic need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and methodology for fake news detection. With the help of Machine Learning, we tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. Even we have presented the mechanism to identify the significant Tweet's attribute and application architecture to systematically automate the classification of the online news.

scholarly journals A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

2019 ◽  
Vol 25 (4) ◽  
pp. 62-67 ◽  
Author(s):  
Feyza Altunbey Ozbay ◽  
Bilal Alatas
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Fake News ◽  
Second Stage ◽  
Novel Approach ◽  
Three Stages ◽  
Last Stage ◽  
Real World Datasets ◽  
First Time ◽  
Metaheuristic Optimization Algorithms

Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.

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