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This chapter asks: How has Donald Trump communicated about COVID-19 on Twitter? How have conspiracy theories influenced his Twitter communication about COVID-19? Utilising critical discourse analysis, it analysed tweets in which Trump communicated about COVID-19 and showed that he used social media to spread conspiracy theories and fake news about COVID-19.

The findings show that Donald Trump uses social media such as Twitter for spreading far-right ideology, conspiracy theories and fake news. He makes use of a variety of linguistic ideological devices. In the context of COVID-19, Trump has spread a variety of conspiracy theories to his millions of followers, which has contributed to the intensification of risks and harms at the time of the worst global health crisis in 100 years.

Since the end of 2016, “fake news” has had a clear meaning in the USA. After years of scholarship attempting to define “fake news” and where it fits among the larger schema of media hoaxing and deception, popular culture and even academic studies converged following the 2016 US presidential election to define “fake news” in drastically new ways. The paper aims to discuss these issues.

In light of the recent elections in the USA, many fear “fake news” that have gradually become a powerful and sinister force, both in the news media environment as well as in the fair and free elections. The scenario draws into questions how the general public interacts with such outlets, and to what extent and in which ways individual responsibility should govern the interactions with social media.

Fake news is a growing threat to democratic elections in the USA and other democracies by relentless targeting of hyper-partisan views, which play to the fears and prejudices of people, in order to influence their voting plans and their behavior.

Essentially, “fake news” is changing and even distorting how political campaigns are run, ultimately calling into question legitimacy of elections, elected officials and governments. Scholarship has increasingly confirmed social media as an enabler of “fake news,” and continues to project its potentially negative impact on democracy, furthering the already existing practices of partisan selective exposure, as well as heightening the need for individual responsibility.

This paper takes a long-term view of how the US public and private sectors have been viewed in relation to each other. It notes that since the time of approximately the Nixon Administration, each sector has not been viewed favourably by the public. Over the past 40 years, the private sector has been perceived as being run by the unscrupulous and the public sector by incompetents. The essay argues that Donald Trump was able to exploit these circumstances to win the 2016 election.

This paper presents a polemic. It relies on archival research and data to create a new view of historical eras in US business history. The object of analysis is the idea of relative legitimacy, the public image of the State vis-a-vis business and business managers.

Although the paper addresses business history, a novel argument is presented about the 2016 US Presidential election. It is proposed that Trump took advantage of unique historical circumstances; therefore, his win had more to do with the moment than with him personally.

The paper interprets the 2016 Presidential race as the end-point of a 250-year journey. It sets a new agenda, in that previous analyses have mostly viewed the ascendancy of Trump as pertaining to distinctively post-industrial twenty-first-century phenomena.

In analysing the 2016 Presidential race, the emphasis is largely removed from issues of personality or partisan politics.

The paper takes a view of the 2016 election which has not hitherto been adopted. It proposes a new concept – relative legitimacy – as having a substantial explanatory value.

This paper aims to examine how exposure to a presidential candidate's high engagement Instagram images influences a citizen's candidate evaluations.

Data were collected via Amazon MTurk. A 3 × 2 experimental design was employed to test the persuasive effect of exposure of the “most liked” and “most commented on” images of the top four 2016 US presidential primary candidates on a US citizen's candidate evaluation.

Results reveal that highly engaging Instagram images of unfamiliar presidential candidates positively influenced candidate evaluations. However, the same was not true for more well-known presidential candidates.

This study was not conducted during a live campaign and only examined four of the top 2016 presidential primary candidates.

The research includes implications for marketers seeking to increase engagement and reach in Instagram marketing campaigns. This study shows that even brief exposure to a highly engaged post involving an unfamiliar person/product on social media can significantly alter evaluations of that person or product.

To the authors' knowledge, no experimental designs have addressed how Instagram posts influence users' political attitudes and behaviors within the political marketing and communications literature.

Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

Misinformation is notoriously difficult to combat. Although social media firms have focused on combating the publication of misinformation, misinformation accusations, an important by-product of the spread of misinformation, have been neglected. The authors offer insights into factors contributing to the spread of misinformation accusations on social media platforms.

The authors use a corpus of 234,556 tweets about the 2020 US presidential election (Study 1) and 99,032 tweets about the 2022 US midterm elections (Study 2) to show how the sharing of misinformation accusations is explained by locomotion orientation.

The study findings indicate that the sharing of misinformation accusations is explained by writers' lower locomotion orientation, which is amplified among liberal tweet writers.

Practitioners and policymakers can use the study findings to track and reduce the spread of misinformation accusations by developing algorithms to analyze the language of posts. A limitation of this research is that it focuses on political misinformation accusations. Future research in different contexts, such as vaccines, would be pertinent.

The authors show how social media firms can identify messages containing misinformation accusations with the potential to become viral by considering the tweet writer's locomotion language and geographical data.

Early identification of messages containing misinformation accusations can help to improve the quality of the political conversation and electoral decision-making.

Strategies used by social media platforms to identify misinformation lack scale and perform poorly, making it important for social media platforms to manage misinformation accusations in an effort to retain trust. The authors identify linguistic and geographical factors that drive misinformation accusation retweets.

Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement.

The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository.

The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet’s emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation (“we-talk”) is consistently associated with more likes, comments and retweets in the overall sample and subsamples.

The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet’s author moderates these relationships.