scholarly journals Testing Children and Adolescents’ Ability to Identify Fake News: A Combined Design of Quasi-Experiment and Group Discussions

Societies ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 71
Author(s):  
Elena-Alexandra Dumitru
Keyword(s):  
Social Media ◽  
Children And Adolescents ◽  
New Technologies ◽  
Experimental Situation ◽  
Online Environment ◽  
Fake News ◽  
Group Discussions ◽  
Online Source ◽  
Acute Form ◽  
The Given

Nowadays, people increasingly choose to turn to the Internet and especially to social media for news and other types of content, while often not questioning the trustworthiness of the information. An acute form of this problem is that children and adolescents tend to include the use of new technologies in all the aspects of their daily life, yet most of them are unable to distinguish between fake news and trustful information in an online environment. This study is based on a Dutch empirical study and was conducted in Romania to examine whether schoolchildren and adolescents were able to identify a hoax website as fake, using a self-administrative questionnaire and open group discussions about the given online source. Similar to other studies based on the same research design, this research aims to explore the vulnerability of students to fake news and the way they experience an experimental situation in which they are exposed to online fake information. This exploratory study revealed that both children and adolescents are not preoccupied with the trustworthiness of the information they are exposed to in social media. While only 4 of the 54 students stated that they would not choose to save a fake animal (from a hoax website), all four of them had reasons that proved that they did not perceive the information as being a hoax. Thus, participants proved that they would act upon being exposed to fake information even when they do not trust the source.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Countering the New Media Podia: Youth and ‘Fake News’ in Kenya

2021 ◽  
Vol 8 (4) ◽  
pp. 8-12
Author(s):  
Oloo Ong’ong’a
Keyword(s):  
Social Media ◽  
New Media ◽  
New Technologies ◽  
Information Age ◽  
Fake News ◽  
The Public ◽  
The Social ◽  
Legal Structures ◽  
Media Platform ◽  
The Media

The rise of fake news into the new media platform has raised significant concern in Africa and Kenya in recent years. The new media has embedded itself with fake news, which sometimes has led to the misunderstanding and misinformation of particular events that might be of the public interest. The general public, policymakers, and scholars, as well as the media, have found this as a very challenging issue. The upsurge of the new technologies, mainly social media, has posed challenges as youth immerse themselves in utilizing these social media for their own benefits. This is coupled with the creation and spreading of fake news, which sometimes when it goes viral; they lead to stress, panic and uncertainty to the individuals that come across them. The ability of users’ exceptional capacity to produce, reproduce, and distribute their information to a broad audience makes social media, an essential tool in the information age. The article critically reviews the literature on fake news and recommends for media literacy, strengthening the legal structures and use of sophisticated technologies as a strategy to fight fake news in the social media in Kenya.

Social Media and Elections in Africa

2019 ◽  
Author(s):  
Iginio Gagliardone
Keyword(s):  
Social Media ◽  
New Media ◽  
New Technologies ◽  
Fake News ◽  
Playing Field ◽  
The Past ◽  
Global North ◽  
The One ◽  
The Relationship

The analysis of the diffusion of social media in Africa and its relevance for politics has been caught in a paradox. On the one hand, social media have been saluted for their newness and for their ability, especially in connection with increasingly accessible portable tools such as mobile phones, to offer a level playing field for individuals to participate in politics and speak to power. On the other hand, this very enthusiasm has evoked relatively tired tropes used to frame the advent of other “new” technologies in the past, stressing what they could do to Africa, rather than exploring what they are doing in Africa. Early research on the relationship between social media and elections in Africa has tended to adopt normative frameworks adapted from the analysis of electoral contests in the Global North, presupposing unfettered citizens using social media to root for their leaders or demand accountability. A more recent wave of empirically grounded studies has embraced a greater conceptual and methodological pluralism, offering more space to analyze the contradictions in how social media are used and abused: how humor can be turned into a powerful tool to contest a type of power that appears overwhelming; or how armies of professional users have exploited people’s credulity of new media as “freer” from power to actually support partisan agenda. Interestingly, this latter approach has brought to light phenomena that have only recently caught global attention, such as the role of “fake news” and misinformation in electoral contests, but have played a determinant role in African politics for at least a decade.

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 New technologies, Social Media and Democracy

2021 ◽  
Vol 20 (41) ◽  
pp. 253-274
Author(s):  
Edilene Lôbo ◽  
José Luis Bolzan de Morais
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Political Parties ◽  
New Technologies ◽  
Fake News ◽  
Human Right ◽  
Traditional Media ◽  
Free Thought ◽  
The Impact

This article aims to consider the impact of new technologies in the Brazilian elections of 2018, questioning about the possibilities of its transformation with the prominent use of social networks to directly connect citizens and candidates, without the customary intervention of political parties and traditional media. It also aims to discuss the role of fake news in the electoral process and the means to fight it, so it does not denature the free thought formation as a human right essential to the practice of citizenship in the new digital age.

scholarly journals A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 1046-1050
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
New Technologies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Fake News ◽  
Detection Techniques ◽  
Social Media Platforms ◽  
Extensive Growth

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

scholarly journals STUDY OF THE DISTRIBUTION OF FAKE NEWS (HOAX) THROUGH SOCIAL MEDIA RELATED TO THE COVID-19 VACCINE AND PREVENTION EFFORTS TO SUPPORT MASS EXECUTION VACCINE IN INDONESIA

2021 ◽  
Vol 6 (24) ◽  
pp. 18-29
Author(s):  
Gideon Satria Putra Sugiyanto ◽  
Annisa Sabrina Nur Arrasy ◽  
Sweeta Melanie
Keyword(s):  
Social Media ◽  
Fake News ◽  
Group Discussions ◽  
The Public ◽  
Counter Narrative ◽  
Social Media Platforms ◽  
The Government ◽  
Negative Impacts ◽  
Media Channels ◽  
Depth Interviews

The COVID-19 pandemic has been going on in Indonesia for more than a year since the beginning of 2020. This pandemic has certainly had many negative impacts, both macro, and micro. The Indonesian government has made a lot of efforts to tackle this pandemic both operationally and in socialization to reduce the further spread of vaccine efforts throughout Indonesia. But unfortunately, there is the challenge of spreading fake news related to the COVID-19 vaccine that is troubling the public. The spread of fake news happened quite quickly with digital communication using social media. Research using qualitative methods examines the condition of socialization communication related to the COVID-19 vaccine, fake news, and efforts to overcome it through in-depth interviews and focus group discussions. The results of the study show that there has been a lot of communication and socialization carried out by the government regarding the COVID-19 vaccine but it has not been structured in one source and there is still minimal anticipation of fake news. As a result, a lot of fake news has spread widely in the community regarding COVID-19, starting from issues related to health and beyond health or safety. Efforts that can be made to minimize the spread of fake news and its dangers include collaboration with various stakeholders, mass and periodic socialization and education on various social media channels, strict penalties for spreading fake news, providing social media platforms or channels to file complaints, create educational content and creative counter-narrative.

To Tweet or Not To Tweet?

2014 ◽  
Vol 43 (1) ◽  
pp. 40-41
Author(s):  
James LoRusso
Keyword(s):  
Social Media ◽  
Learning Process ◽  
New Technologies ◽  
Economic Context

This piece argues that new technologies generally, and social media in particular, are too often accepted uncritically and incorporated hastily into course designs for the humanities. The author encourages teachers to ask two basic questions when considering social media in the classroom: 1. Does social media actually improve the learning process? And 2. How are these technologies embedded in the larger socio-economic context?

Says Who? The Effects of Presentation Format and Source Rating on Fake News in Social Media

MIS Quarterly ◽  
2019 ◽  
Vol 43 (3) ◽  
pp. 1025-1039 ◽  
Author(s):  
Antino Kim ◽  
◽  
Alan R. Dennis ◽  
Keyword(s):  
Social Media ◽  
Presentation Format ◽  
Fake News

Identity concerns drive belief: The impact of partisan identity on the belief and dissemination of true and false news

2018 ◽  
Author(s):  
Andrea Pereira ◽  
Jay Joseph Van Bavel ◽  
Elizabeth Ann Harris
Keyword(s):  
Social Media ◽  
Political System ◽  
Political Identity ◽  
Social Goals ◽  
Fake News ◽  
Political Beliefs ◽  
System P ◽  
False News ◽  
Democrats And Republicans ◽  
The Impact

Political misinformation, often called “fake news”, represents a threat to our democracies because it impedes citizens from being appropriately informed. Evidence suggests that fake news spreads more rapidly than real news—especially when it contains political content. The present article tests three competing theoretical accounts that have been proposed to explain the rise and spread of political (fake) news: (1) the ideology hypothesis— people prefer news that bolsters their values and worldviews; (2) the confirmation bias hypothesis—people prefer news that fits their pre-existing stereotypical knowledge; and (3) the political identity hypothesis—people prefer news that allows their political in-group to fulfill certain social goals. We conducted three experiments in which American participants read news that concerned behaviors perpetrated by their political in-group or out-group and measured the extent to which they believed the news (Exp. 1, Exp. 2, Exp. 3), and were willing to share the news on social media (Exp. 2 and 3). Results revealed that Democrats and Republicans were both more likely to believe news about the value-upholding behavior of their in-group or the value-undermining behavior of their out-group, supporting a political identity hypothesis. However, although belief was positively correlated with willingness to share on social media in all conditions, we also found that Republicans were more likely to believe and want to share apolitical fake new. We discuss the implications for theoretical explanations of political beliefs and application of these concepts in in polarized political system.

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