scholarly journals Incoming Undergraduate Students Struggle to Accurately Evaluate Legitimacy of Online News

2021 ◽  
Vol 16 (1) ◽  
pp. 95-97
Author(s):  
Sarah Bartlett Schroeder

A Review of: Evanson, C., & Sponsel, J. (2019). From syndication to misinformation: How undergraduate students engage with and evaluate digital news. Communications in Information Literacy, 13(2), 228-250. https://doi.org/10.15760/comminfolit.2019.13.2.6 Abstract Objective – To determine how new undergraduate students access, share, and evaluate the credibility of digital news. Design – Asynchronous online survey and activity. Setting – A small private, liberal arts college in the southeastern United States of America. Subjects – Participants included 511 incoming first-year college students. Methods – Using the Moodle Learning Management System, incoming first-year students completed a mandatory questionnaire that included multiple choice, Likert scale, open-ended, and true/false questions related to news consumption. Two questions asked students to identify which news sources and social networking sites they have used recently, and the next two questions asked students to define fake news and rate the degree to which fake news impacts them personally and the degree to which it impacts society. The end of the survey presented students with screenshots of three news stories and asked them to reflect on how they would evaluate the claim in the story, their confidence level in the claim, and whether or not they would share this news item on social media. The three items chosen represent certain situations that commonly cause confusion for news consumers: (a) a heading that does not match the text of the article, (b) a syndicated news story, and (c) an impostor URL and fake news story. Researchers coded the student responses using both preset and emergent codes. Main Results – Eighty-two percent of students reported using at least one social media site to access political news in the previous seven days. Students reported believing that fake news is a worrying trend for society, with 86% labelling it either a “moderate” or “extreme” barrier to society’s ability to recognize accurate information. However, they expressed less concern about their own ability to navigate an information environment in which fake news is prevalent, with 51% agreeing that it has only somewhat of an effect on their own ability to effectively navigate digital information. Of the three news items presented to them, students expressed the least confidence (an average of 1.55/4) and least interest in sharing (12%) the first news item, in which the heading does not match the text. However, only 14% of respondents noted this mismatch. In evaluations of the second item, an AP news item on the Breitbart website, 35% of students noted the website on which the article was found, but fewer noted that the original source is the Associated Press. Student responses to the third article, a fake news item from a website masquerading as an NBC website, show that 37% of students believed the source to come from a legitimate NBC source. Only 7% of students recognized the unusual URL, and 24% of respondents indicated that they might share this news item on social media. Conclusion – The study finds that impostor URLs and syndicated news items might confuse students into misevaluating the information before them, and that librarians and other instructors should raise awareness of these tactics.

2021 ◽  
Vol 10 (5) ◽  
pp. 170
Author(s):  
Reinald Besalú ◽  
Carles Pont-Sorribes

In the context of the dissemination of fake news and the traditional media outlets’ loss of centrality, the credibility of digital news emerges as a key factor for today’s democracies. The main goal of this paper was to identify the levels of credibility that Spanish citizens assign to political news in the online environment. A national survey (n = 1669) was designed to assess how the news format affected credibility and likelihood of sharing. Four different news formats were assessed, two of them linked to traditional media (digital newspapers and digital television) and two to social media (Facebook and WhatsApp). Four experimental groups assigned a credibility score and a likelihood of sharing score to four different political news items presented in the aforementioned digital formats. The comparison between the mean credibility scores assigned to the same news item presented in different formats showed significant differences among groups, as did the likelihood of sharing the news. News items shown in a traditional media format, especially digital television, were assigned more credibility than news presented in a social media format, and participants were also more likely to share the former, revealing a more cautious attitude towards social media as a source of news.


2020 ◽  
Vol 9 (1) ◽  
pp. 1572-1575

Fake news is a coinage often used to refer to fabricated news that uses eye-catching headlines for increased sales rather than legitimate well-researched news, spread via online social media. Emergence of fake news has been increased with the immense use of online news media and social media. Low cost, easy access and rapid dissemination of information lead people to consume news from social media. Since the spread rate of these contents are faster it becomes difficult to identify the fake news from the accurate information. People can download articles from sites, share the content, re-share from others and by the end of the day the false information has gone far from its original site that it becomes very difficult to compare with the real news. It is a long standing problem that affects the digital social media due to its serious threats of misleading information, it creates an immense impact on the society. Hence the identification of such news are relevant and so certain measures needs to be taken in order to reduce or distinguish between the real and fake news. This paper provides a survey on recent past research papers done on this domain and provides an idea on different techniques on machine learning and deep learning that could help in the identification of fake and real news.


10.2196/13076 ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. e13076
Author(s):  
Sara Melvin ◽  
Amanda Jamal ◽  
Kaitlyn Hill ◽  
Wei Wang ◽  
Sean D Young

Background Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. Objective This study aimed to determine whether social media data can be used to monitor sleep deprivation. Methods The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. Results Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet’s author with an average area under the curve of 0.68. Conclusions It is feasible to use social media to identify students’ sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health.


2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi

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.


PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250419
Author(s):  
Taichi Murayama ◽  
Shoko Wakamiya ◽  
Eiji Aramaki ◽  
Ryota Kobayashi

Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.


2019 ◽  
Vol 5 (2) ◽  
pp. 205630511984750 ◽  
Author(s):  
Brooke E. Auxier ◽  
Jessica Vitak

With the influx of content being shared through social media, mobile apps, and other digital sources—including fake news and misinformation—most news consumers experience some degree of information overload. To combat these feelings of unease associated with the sheer volume of news content, some consumers tailor their news ecosystems and purposefully include or exclude content from specific sources or individuals. This study explores customization on social media and news platforms through a survey ( N = 317) of adults regarding their digital news habits. Findings suggest that consumers who diversify their online news streams report lower levels of anxiety related to current events and highlight differences in reported anxiety levels and customization practices across the political spectrum. This study provides important insights into how perceived information overload, anxiety around current events, political affiliations and partisanship, and demographic characteristics may contribute to tailoring practices related to news consumption in social media environments. We discuss these findings in terms of their implications for industry, policy, and theory.


2018 ◽  
Vol 47 (2) ◽  
pp. 251-275 ◽  
Author(s):  
Yu-Leung Ng ◽  
Xinshu Zhao

By adopting the uses and gratifications approach to understand two evolutionary needs—the environmental surveillance need and social involvement need—this study investigated the use of alarm and prosocial words in news headlines and the associated generic digital footprints. We analyzed over 170,000 online news headlines and the number of associated clicks and “likes” for each news story on an online news platform. Our results support the idea of a human alarm system for sensational news as a psychological survival mechanism designed to detect and pay attention to threatening news such as catastrophes and diseases. News headlines with alarm words indirectly attracted more “likes,” indicating a concern with survival, through an increased number of clicks to select that news item. Furthermore, the results of a conditional indirect effect model showed that while online readers selectively clicked on news headlines with alarm words, the presence of a prosocial word in the headline increased the likelihood that readers would “like” it.


Author(s):  
Dipti Chaudhari ◽  
Krina Rana ◽  
Radhika Tannu ◽  
Snehal Yadav

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.


2020 ◽  
Vol 45 (s1) ◽  
pp. 694-717
Author(s):  
Nicoleta Corbu ◽  
Alina Bârgăoanu ◽  
Raluca Buturoiu ◽  
Oana Ștefăniță

AbstractThis study examines the potential of fake news to produce effects on social media engagement as well as the moderating role of education and government approval. We report on a 2x2x2 online experiment conducted in Romania (N=813), in which we manipulated the level of facticity of a news story, its valence, and intention to deceive. Results show that ideologically driven news with a negative valence (rather than fabricated news or other genres, such as satire and parody) have a greater virality potential. However, neither the level of education nor government approval moderate this effect. Additionally, both positive and negative ideologically driven news stories enhance the probability that people will sign a document to support the government (i. e., potential for political engagement on social media). These latter effects are moderated by government approval: Lower levels of government approval lead to less support for the government on social media, as a consequence of fake news exposure.


2021 ◽  
Vol 23 (2) ◽  
pp. 165
Author(s):  
Zikra Yanti

The effects of internet use bring with it many negative aspects linked to online fake news in Indonesia. Indonesia's fight against the spread of online fake news has been going on for many years. However, in 2017, the country experienced the biggest challenges in the bid to battle and resolve the rise of post-truth politics in the country. In addition, the spread of fake news in Islam is prohibited and perspectives from Islamic law equally discouraged the same. There is no harm in making gossip focused on sharing real experiences and emotions but Islam forbids any information being made with the intention of spreading rumors or falsehood. Therefore, the aim of this paper is to discuss online fake news based on Indonesia Law and Islamic Perfectives. The study conducts descriptive analytical literature review methods without using a basic assumption or proposition. Also, the literature used by the author for data collection includes primary and secondary sources from previous studies, such as publications, reference books, online news verification; and ayahs from Qur’an & Hadith that are centered on Indonesia Cyber Crime Law Settings. Cybercriminal offense governed in Law No. 11 Year 2008 on Information and Electronic Transactions (UUITE) relating to online fake news item number one: criminal offenses involved in illegal activities, such as: distribution or propagation, transmission, unavailability of illegal content, including: ethics (Article 27[1] UUITE), gambling (Article 27 [2] UUITE); disrespect or defamation (Article 27 [3] UUITE); outrage or threats (Article 27 [4] UUITE), hoax manipulating and damaging customers (Article 28 [1] UUITE); creates a sense of ethnic hostility-based bigotry (Article 28 [2] UUITE). Equally, online fake news is also not allowed in Islam and that is evident in some ayahs stated in the Qur’an, which among are: Qur’an (49:6) & (24:15). Since online fake news has to do with spreading lie, falsehood, rumors and gossips, Islam condemns all kinds of deceit. Therefore, spreading rumors should not be treated as trivial or casual nor be encouraged as a form entertainment due to the high concerns it can raise and its far-reaching implications.


Sign in / Sign up

Export Citation Format

Share Document