The “Big, Beautiful Bill:” the Ultimate Simulacrum or Post‐Truth Exercise in Orwellian Doublespeak—Newspeak

Prior to the 2020 election President Trump suggested the election should be postponed “until the country can make sure that only eligible American citizens can vote.” With the COVID‐19 pandemic leading many states to take steps that made it easier for citizens to vote safely, the president and his allies made numerous false claims about voter fraud; others argued that voter fraud is not common and is unlikely to appreciably increase with greater reliance on mail balloting. We rely on a national Internet‐based survey experiment conducted prior to the 2020 election to assess the effectiveness of both messages on citizens' support for a hypothetical proposal to postpone the presidential election. The results suggest that respondents were more likely to support postponement if they received a fake news message that fraud is common. The results also suggest that these effects are conditional; both political party and knowledge moderate the relationship.Related ArticlesAguado, N. Alexander. 2022. “When Charismatic Leadership Trumps Social Networking: Searching for the Effects of Social Media on Beliefs of Electoral Legitimacy.” Politics & Policy 50(5): 942–51. https://doi.org/10.1111/polp.12494.Fisher, Patrick. 2020. “Generational Replacement and the Impending Transformation of the American Electorate.” Politics & Policy 48(1): 38–68. https://doi.org/10.1111/polp.12340.Stockemer, Daniel. 2013. “Corruption and Turnout in Presidential Elections: A Macro‐Level Quantitative Analysis.” Politics & Policy 41(2): 189–212. https://doi.org/10.1111/polp.12012.

ABSTRACTIn recent election cycles, especially during the 2020 US presidential election cycle, individuals displayed a renewed and heightened sense of civic, social, and political involvement. Voter turnout was high in the 2020 presidential election, the highest for any other presidential election since 1900. I submit that the psychological influences of populism and psychological involvement foster this high turnout. Using the 2020 American National Election Study, this article examines their influences on political behavior. It places greater focus on populism because scholars of American elections rarely test it as an individual‐level force in presidential elections and because scholars do not examine it with psychological involvement simultaneously. I find that populism and psychological involvement are distinct concepts, both boost voter turnout, and they shape vote choice and candidate affect.Related ArticlesAguado, N. A. 2022. “When Charismatic Leadership Trumps Social Networking: Searching for the Effects of Social Media on Beliefs of Electoral Legitimacy.” Politics & Policy 50, no. 5: 942–951. https://doi.org/10.1111/polp.12494Craig, S. C., and J. Gainous. 2024. “To Vote or Not to Vote? Fake News, Voter Fraud, and Support for Postponing the 2020 U.S. Presidential Election.” Politics & Policy 52, no. 1: 33–50. https://doi.org/10.1111/polp.12577Deegan‐Krause, K., and T. Haughton. 2009. “Toward a More Useful Conceptualization of Populism: Types and Degrees of Populist Appeals in the Case of Slovakia.” Politics & Policy 37, no. 4: 821–841. https://doi.org/10.1111/j.1747‐1346.2009.00200.x

Fake news is a serious problem because it misinforms people about important issues. The present study examined belief in false headlines about election fraud after the 2020 U.S. presidential election. Belief in election fraud had dangerous consequences, including the deadly insurrection at the U.S. Capitol in January 2021. In the present study, participants rated the truthfulness of true and false headlines about the election, and then completed individual difference measures eight days after the election. Participants with more conservative ideology, greater presidential approval of the outgoing president, greater endorsement of general conspiracy narratives and poorer cognitive reflection demonstrated greater belief in false headlines about election fraud. Additionally, consuming more politically conservative election news was associated with greater belief in false headlines. Identifying the factors related to susceptibility to false claims of election fraud offers a path toward countering the influence of these claims by tailoring interventions aimed at decreasing belief in misinformation and decreasing conspiracy beliefs to those most susceptible.

The spread of fake news on social media platforms has garnered much public attention and apprehension. Consequently, both the tech industry and academia alike are investing increased effort to understand, detect, and curb fake news. Yet, researchers differ in what they consider to be fake news sites. In this paper, we first aggregate 5 lists of fake and 3 of mainstream news sites published by experts and reputable organizations. Then, focusing on tweets about the democratic (Hillary Clinton) and republican (Donald Trump) nominees in the 2016 U.S. presidential election, we use each pair of fake and traditional news lists as an independent “groundtruth” to examine i) the prevalence, ii) temporal characteristics and iii) the agenda-setting differences between fake and traditional news sites. We observe that depending on the groundtruth, the prevalence of fake news varies significantly. However, the temporal trends and agenda-setting differences between fake and mainstream news sites remain moderately consistent across different groundtruth lists.

This study determined the antecedents of diffusion scope (total audience), speed (number of adopters/time), and shape (broadcast vs. person-to-person transmission) for true vs. fake news about a falsely claimed stolen 2020 US Presidential election across clusters of users that responded to one another’s tweets (“user clusters”). We examined 31,128 tweets with links to fake vs. true news by 20,179 users to identify 1,069 user clusters via clustering analysis. We tested whether attributes of authors (experience, followers, following, total tweets), time (date), or tweets (link to fake [vs. true] news, retweets) affected diffusion scope, speed, or shape, across user clusters via multilevel diffusion analysis. These tweets showed no overall diffusion pattern; instead, specific explanatory variables determined their scope, speed, and shape. Compared to true news tweets, fake news tweets started earlier and showed greater broadcast influence (greater diffusion speed), scope, and person-to-person influence. Authors with more experience and smaller user clusters both showed greater speed but less scope and less person-to-person influence. Likewise, later tweets showed slightly more broadcast influence, less scope, and more person-to-person influence. By contrast, users with more followers showed less broadcast influence but greater scope and slightly more person-to-person influence. These results highlight the earlier instances of fake news and the greater diffusion speed of fake news in smaller user clusters and by users with fewer followers, so they suggest that monitors can detect fake news earlier by focusing on earlier tweets, smaller user clusters, and users with fewer followers.

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.

The spread of conspiracy theories and misinformation poses substantial threats to democracy around the world. In the United States, entrenched political polarization is both a consequence and a ramification of the spread of biased and false information. Much of this misinformation is spread online, especially on social media. Of all the social media networks in existence, the video-sharing platform YouTube is the most significant incubator of right-wing conspiracist thinking. To what extent has internet usage affected conspiracy-mindedness in the U.S. during the Trump era? I analyze data from five waves of the Pew Research Center’s “American Trends Panel” to test the hypotheses that (1) being perpetually online, (2) keeping many social media accounts, and (3) relying on YouTube for news will increase perceptions of ‘fake news’, stoke conspiracist thinking, and help make democracy’s status in the U.S. ever more precarious. Findings indicate that reliance on YouTube for news is an especially powerful predictor of noticing fake news about COVID-19 and the 2020 U.S. presidential election; attitudes about voter fraud, Donald Trump’s challenges to the election results, and the January 6, 2021, insurrectionists; and deciding to stop talking to someone because of politics.

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 spread of content produced by fake news publishers was one of the most discussed characteristics of the 2016 U.S. Presidential Election. Yet, little is known about the prevalence and focus of such content, how its prevalence changed over time, and how this prevalence related to important election dynamics. In this paper, we address these questions using tweets that mention the two presidential candidates sampled at the daily level, the news content mentioned in such tweets, and open-ended responses from nationally representative telephone interviews. The results of our analysis highlight various important lessons for news consumers and journalists. We find that (i.) traditional news producers outperformed fake news producers in aggregate, (ii.) the prevalence of content produced by fake news publishers increased over the course of the campaign-particularly among tweets that mentioned Clinton, and (iii.) changes in such prevalence were closely following changes in net Clinton favorability. Turning to content, we (iv.) identify similarities and differences in agenda setting by fake and traditional news media and show that (v.) information individuals most commonly reported to having read, seen or heard about the candidates was more closely aligned with content produced by fake news outlets than traditional news outlets, in particular for information Republican voters retained about Clinton. We also model fake-ness of retained information as a function of demographics characteristics. Implications for platform owners, news consumers, and journalists are discussed.

The flow of misinformation and disinformation around the 2016 U.S. presidential election put the problem of “fake news” on the agenda all over the world. As a result, news organizations and companies have taken measures to reduce or eliminate the production and dissemination of fake news. Linguistic Inquiry and Word Count (LIWC) software was employed in the current study to examine 1,500 randomly selected tweets that were used to influence the 2016 U.S. presidential election. Results showed fake news are less likely to have analytical thinking. Moreover, both alt-Right troll accounts and alt-Left troll accounts posted fake news on Twitter. Lastly, Cluster analysis revealed that the fake news tweets are more likely to be retweeted and use fewer analytical thinking.
 APA Citation
 Padda, K. (2020). Fake news on Twitter in 2016 U.S. presidential election: a quantitative approach. The Journal of Intelligence, Conflict, and Warfare, 3(2), 18-45. https://journals.lib.sfu.ca/index.php/jicw/article/view/2374/1810.

The purpose of this paper is to analyze 1) who created and distributed fake news, 2) the distribution channels of fake news, 3) who fake news has targeted, and 4) the effects on voting and the impact of fake news on Korean politics. In South Korea, fake news was mainly created by candidates or election campaigns. The reason is that in the wake of the impeachment of President Park Guen Hye, all the political parties in Korea used fake news as a means of mobilizing supporters for each of their candidates or parties to gain an advantage in situations involving political divisions and confrontations between the pro-impeachment, progressive young generation and anti-impeachment, conservative senior generation. Voters' media usage patterns were polarized through social network services (SNS) media and television. Fake news was mostly received through these two media outlets. According to the spreading structure of fake news in Korea, the younger generation generally uses SNS posts intended for unspecified individuals, and the older generation uses closed SNS like KakaoTalk or Naver’s BAND. In the end, it is typically characteristic of the older generation to spread fake news through existing offline human networks. In the 2017 presidential election, fake news has been confirmed to have the effect of mobilizing supporters for each political party. In the presidential election, an increase in voter turnout was confirmed among those in their 20s and those in their 60s or older. Evidently, fake news influenced the election of Moon Jae-In. The influence of fake news is expected to grow further as ideological polarization and consequent political polarization continues to intensify in South Korea.

Since the first case of the Coronavirus was discovered in Lagos State, Nigeria, concerns were raised about the impact of a widespread virus outbreak. These concerns have been cultivated by the weak Nigerian healthcare system. Despite the fact that the virus is not widely known in the nation, its effects on health and wellness are a big cause for concern. This study examined the state of Nigeria’s healthcare system, the general attitude toward the pandemic, and the implications for pandemic containment. In-depth interviews with 45 participants, including key informants who were health workers, were conducted as part of the qualitative method used in this study. The findings of this study found a paucity of medical professionals and materials in Nigerian hospitals and other primary healthcare institutions. It also turned out that some people continue to deny that the virus even exists while ignoring health precautions. This study strongly recommends that social workers participate in community health education initiatives that aim to dispel ingrained cultural and traditional myths about the virus, launch successful grass-roots campaigns to increase public awareness of the devastating Coronavirus pandemic and revitalize cottage hospitals and other primary healthcare facilities, particularly in rural areas.

Since the rising to notoriety of the present ‘genre’ of malicious content peddled as ‘fake news’ (mostly over social media) in 2016 during the United States’ presidential election, barely three years until Nigeria’s 2019 general elections, fake news has made dangerously damaging impacts on the Nigerian society socially, politically and economically. Notably, the escalating herder-farmer communal clashes in the northern parts of the country, ethno-religious crises in Taraba, Plateau and Benue states and the furiously burning fire of the thug-of-war between the ruling party (All Progressives Congress, APC) and the opposition, particularly the main opposition party (People’s Democratic Party, PDP) have all been attributed to fake news, untruth and political propaganda. This paper aims to provide further understanding about the evolving issues regarding fake news and its demonic impact on the Nigerian polity. To make that contribution toward building the literature, extant literature and verifiable online news content on fake news and its attributes were critically reviewed. This paper concludes that fake news and its associated notion of post-truth may continue to pose threat to the Nigerian polity unless strong measures are taken. For the effects of fake news and post-truth phenomena to be suppressed substantially, a tripartite participation involving these key stakeholders – the government, legislators and the public should be modelled and implemented to the letter.

Fabricated news stories that contain false information but are presented as factually accurate (commonly known as ‘fake news’) have generated substantial interest and media attention following the 2016 U.S. presidential election. While the full details of what transpired during the election are still not known, it appears that multiple groups used social media to spread false information packaged in fabricated news articles that were presented as truthful. Some have argued that this campaign had a material impact on the election. Moreover, the 2016 U.S. presidential election is far from the only campaign where fake news had an apparent role. In this paper, work on a counter-fake-news research effort is presented. In the long term, this project is focused on building an indications and warnings systems for potentially deceptive false content. As part of this project, a dataset of manually classified legitimate and deceptive news articles was curated. The key criteria for classifying legitimate and deceptive articles, identified by the manual classification project, are identified and discussed. The identified criteria can be embodied in a natural language processing system to perform illegitimate content detection. The criteria include the document’s source and origin, title, political perspective, and several key content characteristics. This paper presents and evaluates the efficacy of each of these characteristics and their suitability for legitimate versus illegitimate classification. The paper concludes by discussing the use of these characteristics as input to a customized naive Bayesian probability classifier, the results of the use of this classifier and future work on its development.

Contrary to the despair of popular narratives, fake news accounted for limited amounts of news consumption during the 2016 US presidential election. In this chapter, we explore how giving people baseline statistical information about fake news consumption may influence subjective judgments about how much fake news is consumed, who consumes it, and the importance of fake news as a problem. Such baselines may contrast with more extreme existing inferences derived from anecdotal experience, and serve to drive down subjective assessments. Conversely, by raising the salience of fake news, baseline consumption information could increase the intensity of subjective assessments. Using a survey experiment (n = 981), we find that the effects of baseline information on fake news perceptions are likely small. Moreover, without the proper contextualization, this information may do more to exacerbate than to downplay perceived prevalence and concern about fake news. Simultaneous exposure to two baselines (the percent of all Americans exposed to fake news and the average number of articles consumed) increased perception that fake news consumption has increased since 2016, and increased general concern about fake news. We find little evidence that measures of political or cognitive sophistication moderate our treatment effects. We also find sizeable gaps in who the public thinks consumed the most fake news. Not surprisingly, the gaps are driven by the public's own membership in various social categories. Our data show notable in-group biases in perceived consumption across 2016 vote preference, age, and educational attainment subgroups.