Everyday non-partisan fake news: Sharing behavior, platform specificity, and detection.

PurposeThis study aims to understand the allure and danger of fake news in social media environments and propose a theoretical model of the phenomenon.Design/methodology/approachThis qualitative research study used the uses and gratifications theory (UGT) approach to analyze how and why people used social media during the 2016 US presidential election.FindingsThe thematic analysis revealed people were gratified after using social media to connect with friends and family and to gather and share information and after using it as a vehicle of expression. Participants found a significant number of fake news stories on social media during the 2016 US presidential election. Participants tried to differentiate between fake news and real news using fact-checking websites and news sources and interacted with the social media users who posted fake news and became part of the echo chamber. Behaviors like these emerged in the analysis that could not be completely explained by UGT and required further exploration which resulted in a model that became the core of this study.Research limitations/implicationsThis is a small-scale exploratory study with eight diverse participants, findings should not be generalized to larger populations. Time-specific self-reporting of information from social media and fake news during the 2016 US presidential election. Upgrading public policies related to social media is recommended in the study, contributing to burgeoning policy discussions and provides recommendations for both purveyors of social media and public policymakers.Practical implicationsUpgrade in public policies related to social media is recommended in the study and contributes to burgeoning policy discussions and provides recommendations for both purveyors of social media and public policymakers.Social implicationsSocial media users are spending increased time on their preferred platforms. This study increases the understanding of the nature, function and transformation of virtual social media environments and their effects on real individuals, cultures and societies.What is original/of value about the paper?This exploratory study establishes the foundation on which to expand research in the area of social media use and fake news.Originality/valueThis exploratory study establishes the foundation to expand research in the area of social media use and fake news.

Ushering in the contemporary ‘fake news’ crisis, Craig Silverman of Buzzfeed News reported that it outperformed mainstream news on Facebook in the three months prior to the 2016 US presidential elections. Here the report’s methods and findings are revisited for 2020. Examining Facebook user engagement of election-related stories, and applying Silverman’s classification of fake news, it was found that the problem has worsened, implying that the measures undertaken to date have not remedied the issue. If, however, one were to classify ‘fake news’ in a stricter fashion, as Facebook as well as certain media organizations do with the notion of ‘false news’, the scale of the problem shrinks. A smaller scale problem could imply a greater role for fact-checkers (rather than deferring to mass-scale content moderation), while a larger one could lead to the further politicisation of source adjudication, where labelling particular sources broadly as ‘fake’, ‘problematic’ and/or ‘junk’ results in backlash.

The Practice of Journalism in the Digital Age

“Fake news” has become a much-used and much-hyped term in the so-called “post-truth” era that we now live in. It is also much-maligned: it is often blamed for having a disruptive impact on the outcomes of elections and referenda and for skewing democratic public debate, with the 2016 US Presidential elections and Brexit referendum often cited as examples. “Fake news” has also been flagged for fuelling propaganda and “hate speech” and even violence. “Pizzagate” is an infamous example of exceptional circumstances in which a false news story had a central role in a shooting incident. In December 2016, a man in Washington D.C. took it upon himself to “self-investigate” a story (a completely unfounded conspiracy theory) that the Hillary Clinton campaign team was running a paedophile ring from the premises of a pizzeria. Shots were fired and he was arrested and charged with assault and related offences. Given all this bad press, it is perhaps little wonder that “fake news” has become a major preoccupation for international organisations, national law- and policy-makers, the media and media actors, civil society and academia. But what exactly is “fake news” and what is all the fuss about? In addressing these questions, this column will also consider historical and contemporary perspectives on the term and its relationship with human rights.

ABSTRACTA persistent story about the 2016 US presidential election was the preponderance of fake news stories on social media, and on Facebook in particular, that had no basis in fact but were wholly concocted to quickly amass clicks that could be converted into advertising revenues. This study steps outside of arguments about the spread or efficacy of fake news to instead interrogate its symbolic dimensions and its meaning for both journalism and the larger system of political communication. To conceptualize the role of fake news as a particular symbol, this paper approaches the journalistic condemnation of fake news as an ‘informational moral panic.’ This concept builds off Cohen’s classic formulation of moral panics as public anxiety that a particular social threat will lead to declining standards. The ability to define a phenomenon as an informational moral panic is an exercise in cultural power that ascribes deviancy to particular actors while validating others. In the case of fake news, the anxiety is not so much directed toward a particular group but aimed at the larger transformation of informational spaces made possible by social media. An examination of journalists’ responses in the US press during November 2016 reveals four domains of focus ‒ production, platform, subsidy, and consumption – each with its own narratives of blame and remedy. Fake news becomes a particular signifier that condenses broader concerns surrounding the eroding boundaries of traditional journalistic channels, click-driven news, the extension of mediated voices, and the growing role of social media in news distribution.

Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance between the headline and text bodies. There exists a significant difference between news article headline and text body, since headlines with several key phrases are usually much shorter than the text bodies. Such an information imbalance challenge may cause serious problems for the stance detection task. Furthermore, news article data in online social networks is usually exposed to various types of noise and can be contaminated, which poses more challenges for the stance detection task. In this paper, we propose a novel fake news stance detection model, namely Adversarial Pseudo-Siamese Network model (APSN), to solve these challenges. With coupled input components with imbalanced parameters, APSN can learn and compute feature vectors and similarity score of news article headlines and text bodies simultaneously. In addition, by adopting adversarial setting, besides the regular training set, a set of noisy training instances will be generated and fed to APSN in the learning process, which can significantly enhance the robustness of the model. Extensive experiments have been conducted on a real-world fake news dataset, and the experimental results reveal that the presented model exceeds compared suspicious information detection models with significant advantages.

The Impact of the Supply of Fake News on Consumer Behavior During the 2016 US Election

This article offers a review of scholarly research on the phenomenon of fake news. Most studies have so far focused on three main themes: the definition and the scope of the problem; the potential causes; and the impact of proposed solutions. First, scholarly research has defined fake news as a form of falsehood intended to primarily deceive people by mimicking the look and feel of real news. While initial research has shown that only a small fraction of the online audience is exposed to fake news, for this small group of individuals, the impact of fake news can be quite substantial. Second, studies have identified cognitive processes that make individuals more prone to the influence of fake news, such as confirmation bias, selective exposure, and lack of analytical thinking. Fake news also derives its power from its appeal to partisanship, perceived novelty, and repeated exposure facilitated by both bots and human users that share them in the online sphere. Finally, while fact checking has also risen in response to fake news, studies have found that corrections to wrong information only work on some individuals.

An issue of increasing importance in the past few years has been what is generally referred to as “fake news”. Although there is considerable evidence of such deceptive communication over many centuries, the sheer difference in deception techniques of such communication in an electronic environment has allowed the perpetrators the ability to disguise it in many forms that could not be seen in communication vehicles as print or electronic media such as radio or television. Techniques developed in the context of storable electronic information have allowed fake news items to take on a wider variety of disguises. In addition, with the access to electronic information being available in recent years to a large percentage of the world’s population, the effect of such misleading information has had a much wider sphere of impact. As a consequence, many actors have developed sophisticated tools to convince even very diligent readers of the legitimacy of the false information purveyed. Many examples of this arose in the 2016 United States Presidential election. In particular, many items, supposedly from the Russian government, were aimed at reducing the African-American participation in that election. Our research attempted to assess the effectiveness of those attacks.

Misinformation isn’t definitely new thing; it is way before the inception of social media. It is evolving since 14th century but the term like "fake news", "post truth" are used commonly during movement of 2016 US presidential election. People use social media to read news as it is lost cost and user friendly platform; also it is possible to share news on social media with one click. With this merit, it is also having major disadvantage. If the news is false or misleading news then spread of such news will have adverse consequence on civilization. Therefore, battling fake news is important and has now become developing area of research. Researchers are using Artificial Intelligence based approach such as machine learning and natural language processing to battle with the fake news. This paper presents a comprehensive overview of the earlier detection techniques as well as proposes mathematical model and methodology to improve the result.

Objective To investigate the current status of critical thinking ability and commu-nication ability of nursing students, therefore providing reference for teaching reform. Methods The Critical Thinking Scale translated and revised by Pro. Peng MeiZi of Hong Kong Polytechnic University, and Supportive Communication Scale (SCS) edited by Whetten and his colleague were used to investi-gate nursing students' levels of critical thinking and communication ability among 176 selected nursing students in our college, with method of Random Cluster Sampling. Descriptive analysis, single sample t-test, and two independent sample t-test were applied to the collected data with SPSS l7.0. Results ①The average score of critical thinking for the investigated college nursing students was (264.54±29.94). 72.73% of them got 210~280 points, in which three items: cognitive maturity (t=4.695, P=0.000), open mind (t=4.016, P=0.000), and intellectual curiosity (t=2.973, P=0.003) were greater than 40 points. And the differences were statistically significant; compared to different individual character-istics, being a student cadre (t=3.494, P=0.001) or not is of statistical significance. ②The total average score of communication ability (t=3.219, P=0.002) for college nursing students, as well as the score of providing effective negative feedback (t=6.790, P=0.000) and supportive communication (t=4.267, P=0.000) were significantly higher than median 3.00. And the differences were statistically significant. Conclusion In order to improve the critical thinking ability and interpersonal communication ability of nursing students, and educate the nursing students to adapt to society and complicated clinical nursing environment, it is necessary to reform the curriculum setting, curriculum design and teaching method. Key words: Nursing students; Critical thinking ability; Communication ability; Investiga-tion and study

The term “fake news” came to dominate public political discourse in late 2016 regarding possible efforts by Russian agents to manipulate the US Presidential election. A similar alarm was raised during subsequent European elections the following year. This widespread concern was twisted into an epithet by some political figures, particularly President Trump, to describe traditional news outlets. Eventually, the alarm and name-calling transformed into a serious topic for empirical research, and the initial fruits of that work are beginning to appear. This short article provides a panorama of the scholarship emerging around fake news and illustrates this work by examining in more detail two radically different studies. The article concludes with suggestions for extending this initial research. But first, some background is provided to set the stage.

In contemporary times, social media platforms have been affecting people's political and social lives on a massive scale. On the other hand, these platforms are used by perpetrators of false information to mislead the masses. The phenomenon of fake news and misinformation is not new, but the proliferation of social media has made this issue more pressing. In 2016, the term "fake news" reemerged during US presidential elections. In a political scenario, disinformation and misinformation have the potential to influence political processes. Currently, social media platforms play a very significant role in political communication. Generally, preconceived notions and a set ideology about a political entity play a significant role in information processing. To fulfill the study's objectives, qualitative content analyses have been used. Different fake messages have been collected for content analysis, which went viral during the Indian General Elections 2019. To finalize the different fake messages, the Purposive sampling (non-probability) method has been adopted to fulfill specific criteria. Particularly in this research study, the analysis is based on five different categories or parameters suggested by Mike Cormack in his work on ideology. These five categories are Content, Structure, Absence, Style, and Mode of address. The study focuses on the role of fake messages in setting ideologies and agendas on social media platforms during the 2019 general elections. So, in this research study, different fake messages in the form of images and text have been considered for analysis. The analysis supports the theory of agenda building, and Narrative persuasion theory of communication

Abstract: Math is able to train logical, analytical, systematic, critical, and creative thinking of students. Mathematical critical thinking ability is a capability that supports a variety of other mathematical thinking abilities. The students' critical thinking ability is also closely related to the personality or character of the student. Personality can affect how a person behaves, thinks, or interacts with around. The personality types discussed in this study are examined from four types of temperaments by the theories of Hippocrates and Galenus. This research includes the type of qualitative research that uses descriptive qualitative research methodology. This research will be conducted on the students of class XI MIA SMA Negeri 2 Kota Jambi. The thing described in this research is the students' mathematical critical thinking based on the dominant personality type on linear program solution. The results of personality tests of the instrument adapted from the patterns of personality by Lana Bateman found 40 dominant students that is the type of phlegmatic personality. Furthermore, based on sampling technique that is purposive sampling technique, 2 students are taken as research subjects ie phlegmatic students with the highest level of critical thinking ability called first phlegmatic students and phlegmatic students with the lowest level of critical thinking ability is called second phlegmatic student. From the results obtained can be concluded that the first phlegmatic students have the level of critical thinking skills are very critical and the second phlegmatic students have a critical level of critical thinking is not critical. This is inversely between the ability to think critically and the type of phlegmatic personality. The first phlegmatic student with enough phlegmatic category has critical critical mathematical thinking and the second phlegmatic student with very high / dominant phlegmatic category, critical critical thinking ability is not critical.Keywords: Mathematical Critical Thinking Ability, Four Temperament Types

The digital era has determined a very easy creation and propagation of fake news. As a consequence, it has become harder for people to fight this malicious phenomenon. However, the only weapon that can have results in this informational war is critical thinking. But who should use it? The creators of fake news that do this for different reasons? The social platforms that allow the circulation of fake news with ease? Mass media which does not always verify with much attention and rigour the information they spread? The Governments that should apply legal sanctions? Or the consumer that receives all the fake news, him being the final target? Even if critical thinking would be useful for every actor on fake news’ stage, the one who needs it the most is the consumer. This comes together with the big responsibility placed on his shoulders. Even if others are creating and spreading disinformation, the consumer must be aware and be careful with the information he encounters on a daily basis. He should use his reasoning and he should not believe everything just because it is on the Internet. How can he do that? Critical thinking seems to be a quite difficult tool to use, especially for non-specialized individuals. This paper’s aim is to propose a simplified model of critical thinking that can contribute to detecting fake news with the help of people’s self judgement. The model is based on theories from Informal Logic considering the structure of arguments and on Critical Discourse Analysis theories concerning the patterns found in the content of the information.