Social Media and Fake News in the 2016 Election

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.

Fake news has been the subject of a rapid research response, from a range of fields, given its impact on multiple sectors, the public sphere, and everyday life. The most prominent areas and disciplines contributing research and academic writing on fake news have been journalism, media and cultural studies, media literacy, politics, technology, and education. Whilst the concept is part of a broader concern with misinformation, the term “fake news” came to widespread public attention during the 2016 US presidential election. During the campaign, inaccurate social media posts were spread to large groups of users, a form of “viral” circulation found most prominently on the Facebook platform. A subsequent investigation discovered a large quantity of the posts were generated in the town of Veles in Macedonia, leading to concerns about the automated factory production of messages, including by “bots.” A key development in the use of the term “fake news” was Donald Trump’s adoption of it, following his election, as a negative description of unfavorable media coverage, going so far as to respond to unwanted questions from reporters in press conferences with “you’re fake news.” Fake news is a recent development in a long-established area of persuasive, misleading, or disproportionate mass communication. Research into fake news and analysis of it can be broken down into a set of categories. Political fake news is intended to misinform and influence (a contemporary form of propaganda). Strategic “cyberwarfare” by one nation on another may include spreading false information through fake social media accounts, authored by “bots.” Commercial fake news operates in the form of “clickbait,” whereby advertising revenue is attracted and combined with the economic affordances of user data trading. It is important to recognize that multinational digital corporations integrate this kind of communication into their business models. The distinctive impact of fake news has been to destabilize mainstream news media and provoke a crisis of trust in journalism, contributing to polarized public discourse and an increase in discriminatory communication. Research into fake news and the broader “information disorder” has explored fake news as propaganda, the role of technology, algorithms, and data harnessing in the spreading of fake news; fake news as an existential threat to journalism; fake news as part of the process of undermining or challenging democracy; protection from fake news through verification or “fact-checking” tools and more sustainable, longer term educational approaches to developing resilience to misinformation through media literacy. The term “fake news,” however, has been the subject of disagreement, with journalists, policymakers, educators, and researchers arguing either that it presents an oxymoron as false information cannot be categorized as news as defined by journalistic codes of practice (and thus plays into the hands of those who wish to undermine mainstream media) or that it assumes a “false binary” between real and fake, ignoring the gatekeeping agendas at work in all news production.

The increase in social media use within African media fields has seen a concomitant increase in fears and concerns about “fake news” over the last few years. However, there is little empirical evidence that “fake news” has been as much of a menace as observers would have us believe. Much of the “fake news” excitement is anchored on panic by American news organisations following the 2016 US presidential elections. Nevertheless, recent scholarship shows that the effects of “fake news” on the US elections were largely exaggerated while the role of conventional media actors largely suppressed. This article argues that the circulation of “fake news” is intricately tied to traditional (western) media practices which have themselves been problematic. It contends that “fake news” is not the problem in and of itself, but rather a sign that African media fields need to reimagine how journalism is practised within the continent. As such, it maintains that studying African journalists not just as “carrier groups” (in the Weberian sense) but also as the primary definers of what the boundaries of the “Overton window” should be is more informative on “fake news” effects than the current fixation with social media’s role in disseminating “fake news”.

PurposeThe purpose of this paper is to investigate the social media application and the spread of COVID-19 infodemic in Nigeria.Design/methodology/approachA descriptive survey research design was used for this study. A total of 1,200 social media users, regardless of their professions, were randomly selected for the study betweenmid-June and July 2020. Stratified and purposive sampling techniques were used for this study. The questionnaire was designed using Google form and administered using WhatsApp and Telegram to social media users above 18 years old in Nigeria. The data generated was analyzed using descriptive (frequency count) and inferential (mean) statistics, and was presented in tables.FindingsIt was found that people make use of social media during COVID-19 pandemic for diverse reasons such as listening to announcement to be informed, knowing the necessary measures to take by those infected and spreading up-to-date information on the pandemic. Social media tools were highly used during the COVID-19 pandemic, especially WhatsApp and Zoom. Findings reflected that misinformation was spread on social media. This study also showed that the infodemic associated with COVID-19 is managed by confirming the source of the information before sharing it and trusting information from reliable sources.Research limitations/implicationsThe result of this research will contribute to the body of knowledge on social media application, fake news and the spread of COVID-19 infodemic in Nigeria and beyond.Practical implicationsInfodemic is a disaster in the health sector. The spread of infodemic is capable of misleading people, losing trust in government, health providers and health regulatory authorities. This study will help social media users to know how to properly manage social media infodemic during a pandemic or any health-related situations.Originality/valueThis study is novel as it approaches fake news from a COVID-19 perspective. Very few articles emanate from the developing countries in this area. This was because most of the narrative around fake news previously centered around the Western occurrences such as the Iraqi invasion by the USA, the US presidential elections and BREXIT. COVID-19 has demonstrated that the developing world is not immune from fake news as well. This study, therefore, assessed the management of infodemic associated with COVID-19 in Nigeria.

Fake news — deliberately misleading information — is a hot topic in the media.1 Despite the irony in this, there seems good reason for concern. The Pope’s endorsement of Donald Trump was apparently the most read item of news on Facebook in the 3 months leading up to the US Presidential Election.2 Of course, neither the Pope nor even Denzel Washington did endorse the man who surprised many by winning.3 But an important question was raised: how much are we being duped? A few months ago, the Independent newspaper published its own analysis of fake health news on social media sites. It said: ‘Of the 20 most-shared articles on Facebook in 2016 with the word “cancer” in …

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.

Introduction: panic and disaster In the 1920s the political commentator Walter Lippmann famously wrote: ‘Incompetence and aimlessness, corruption and disloyalty, panic and ultimate disaster must come to any people which is denied an assured access to the facts’ (Lippmann, 1920). This seemed to sum up the mood for many in the last few months of 2016 and through into 2017. Following Donald Trump's victory in the US presidential election, and the result of the referendum about Brexit before that, the issue of ‘fake news’ has come to dominate the real news week. The idea of fake news has emerged as one of the defining concepts of our times, with its influence stretching around the globe (BBC, 2017), and being blamed for everything from a rise in xenophobia (Solomon, 2017) to the all-out undermining of Western democracy (Cheshire, 2016). The issue has become of such public concern that it has led to parliamentary enquiries in the UK (Commons Select Committee, 2017), and the establishment of ‘collaborative journalism verification projects’ (CrossCheck, 2017), as well as major soul-searching by the large technology firms. In this chapter we examine the role that communications technology – and specifically social media – plays in the phenomenon of, and discourse around, fake news, drawing on findings from a research project we conducted into the way people interact on Facebook. Based on the implications of this research we then look at how critical digital literacy education – which combines an understanding of the affordances and implications of digital media with an awareness and sensitivity to the role the media play in everyday social politics – can assist in providing people with the knowledge and resources to make informed decisions about their consumption of information circulated online. Filter bubbles and fake news In the immediate aftermath of Trump's election it was social media companies such as Facebook which came in for particular and persistent criticism for their role in the way that fake news was being spread. Trotter (2016), for instance, wrote, ‘Throughout [2016's] presidential campaign, journalists have focused, correctly, on the power of Facebook to shape, distort, and ultimately control the news and information that inform and educate voters.’

The proliferation of fake news on social media is now a matter of considerable public and governmental concern. In 2016, the UK EU referendum and the US Presidential election were both marked by social media misinformation campaigns, which have subsequently reduced trust in democratic processes. More recently, during the COVID-19 pandemic, the acceptance of fake news has been shown to pose a threat to public health. Research on how to combat the false acceptance of fake news is still in its infancy. However, recent studies have started to focus on the psychological factors which might make some individuals less likely to fall for fake news. Here, we adopt that approach to assess whether individuals who show high levels of ‘emotional intelligence’ (EQ) are less likely to fall for fake news items. That is, are individuals who are better able to disregard the emotionally charged content of such items, better equipped to assess the veracity of the information. Using a sample of UK participants, an established measure of EQ and a novel fake news detection task, we report a significant positive relationship between individual differences in emotional intelligence and fake news detection ability. We also report a similar effect for higher levels of educational attainment, and we report some exploratory qualitative fake news judgement data. Our findings are discussed in terms of their applicability to practical short term (i.e. current Facebook user data) and medium term (i.e. emotional intelligence training) interventions which could enhance fake news detection.

Consuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also enables the widespread of fake news. Due to the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.

Nowadays, social media is a very important thing in our daily lives. People can't even think about a second without social media. Because of their busy life, people depend more on social media for information, thus increasing its popularity. Social media can be considered as a two-sided coin having its own advantage and disadvantage. These media help people to connect with their family or friends around the world. But the other side of the social media has many disadvantages. It can be considered as the cause of many problems in our society. One such major issue is the fake news. People were unable to distinguish the true and fake news and also about the credibility of the news and the news provider. They blindly believe the news without knowing the truth and they share the news with others. As a result, the fake news spread faster than the true news. By this, many people and organizations get affected. So, in a world of increasing fake news, a fake news detection system is an essential thing. This project deals with fake news. The system is a Webapp named ALIKAH- a clickbait and fake news detection system. It is just like social media network where the news providers can provide the news. This system distinguishes the fake and true news among the news provided in the Alikah system. There are three modules in this system-the admin, news providers and the users. Admin manages and monitors the system and its functionalities. News providers can provide the news to this system after getting permission from the admin and the user can view, like, comment, report and subscribe to the news and the news provider. Neural network is used as the classifier. This system detects the fake news by checking the credibility of the news provider, monitoring the comments and also by checking the relation of the heading and content of the news provided. It also helps to detect the fake news spreading on other social media like Facebook, by using its heading and content. This system definitely will be a beneficiary to the people and organizations which get affected by the news and also help to find the providers of these news.

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

Due to the enormous and exponential advancement in the online social network, the triad of Facebook, Twitter and Whatsapp posed a great challenge in the form of fake news in front of us. In recent years many events like false propaganda of the 'US presidential election', opinion spamming in 'Brexit referendum', and long-tail series of viral rumors after many natural calamities around the world, created a lot of chaos and law and order problem. Simultaneously, this rapid explosion of fake news also attracted the attention of different researchers to investigate the real cause of it and thus to developed some tools and techniques to relieve and discover the Rumors across online media as soon as possible. In this regard, the Machine Learning (ML) algorithms and Natural Language Processing (NLP) algorithms emerged as the remarkably vital and essential tool to detect fake news in the current age. NLP when aided with machine learning produced many remarkable results that were possible just by manual fact-checking or by normal text detection process. We have systematically discussed the role of NLP and machine learning in the fake news detection process, and various detection techniques based on these. Basic terminology of NLP and machine learning too explained in brief. At last, we gave light on the future trends, open issues, challenges, and potential research oriented toward NLP and ML-based approaches.

We present an analysis of traffic to websites known for publishing fake news in the months preceding the 2016 US presidential election. The study is based on the combined instrumentation data from two popular desktop web browsers: Internet Explorer 11 and Edge. We find that social media was the primary outlet for the circulation of fake news stories and that aggregate voting patterns were strongly correlated with the average daily fraction of users visiting websites serving fake news. This correlation was observed both at the state level and at the county level, and remained stable throughout the main election season. We propose a simple model based on homophily in social networks to explain the linear association. Finally, we highlight examples of different types of fake news stories: while certain stories continue to circulate in the population, others are short-lived and die out in a few days.

Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives: Web sites and content. Our site analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors and registration timing. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency - inverse document frequency (tf-idf) and Latent Dirichlet allocation (LDA) topic modeling is inefficient in detecting fake news, while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study the Web sites and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.

This exploratory study examines how fake news differs from real news in terms of propagation pattern on social media. It analyzed 40 news (20 fake news + 20 real news) surrounding the 2016 US presidential election that emerged on Twitter. Three major observations were made. First, fake news appeared to be posted more than real news. Second, with time, the growing volume of tweets for fake news indicated its sustainable propagation to achieve a wider reach. Third, the tweet volume of real news dropped drastically after the first day. However, it was not true for fake news. The observations are discussed in light of the related literature.