Studying the Community of Trump Supporters on Twitter during the 2020 US Presidential Election via Hashtags #maga and #trump2020

The aim of this paper is to make a zealous effort towards true prediction of the 2016 US Presidential Elections. We propose a novel technique to predict the outcome of US presidential elections using sentiment analysis. For this data was collected from a famous social networking website (SNW) Twitter in form of tweets within a period starting from September 1, 2016 to October 31, 2016. To accomplish this mammoth task of prediction, we build a model in WEKA 3.8 using support vector machine which is a supervised machine learning algorithm. Our results showed that Donald Trump was likely to emerge winner of 2016 US Presidential Elections.

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.

The impact of the COVID-19 pandemic on the 2020 US presidential election is worth watching. Also, who wins the US presidential election will influence US foreign and international policy. This research aims to examine the 2020 US Presidential Election, focusing on Joe Biden’s victory. The author uses Scopus sources, which are analyzed using VOS viewer, NVivo, SWOT, Literature Review, and political communication theory, resulting in the peak of Joe Biden’s research in Scopus in 2023 experiencing a decline. The author also finds the involvement of Russian researchers who analyze Joe Biden, where these two countries have been rivals for a long time. Joe Biden and his vice president are political actors who have successfully delivered their political campaigns against Donald Trump and his vice president, Mike Pence. Joe Biden overcame the threat during his campaign by maximizing his power using persuasive speeches, social media, in-person campaigning, and powerful appeals to influence and persuade audiences.

Neglected Tropical Diseases and the 2012 US Presidential Election

ABSTRACTObjectiveEllis and Ura report that partisan polarization moderates the relationship between economic performance and incumbent party nominees’ two‐party vote share in US presidential elections between 1952 and 2016. We update their model to include the results of the 2020 election and generate forecasts for the 2024 election based on contemporary political‐economic conditions.MethodsWe re‐estimate Ellis and Ura's polarized times model of incumbent party vote shares in presidential elections, incorporating the results of the 2020 contest between Donald Trump and Joe Biden. The model includes an interaction term between national partisan polarization and economic growth, indicating the extent to which partisan polarization moderates the influence of economic performance on voting in presidential elections.ResultsGreater partisan polarization attenuates the association between economic performance and voting in presidential elections. Incorporating data from the 2020 election does not appreciably change the polarized times model's point estimates or the substantive implications of the 1952–2016 results Ellis and Ura report.ConclusionHigher partisan polarization weakens the influence of economic performance on presidential election outcomes. In the 2024 presidential election, the political‐economic fundamentals favor the Republican Party nominee, Donald Trump, to a greater extent than they would if lower levels of partisan polarization prevailed.

This study is concerned with the linguistic devices used to persuade and manipulate audiences of political discourse in the 2020 US Presidential election. This study focused on Donald Trump's and Joe Biden's rhetorical and framing strategies in their speech and debate. A qualitative method was used to conduct the study to analyze the data. The study aimed to find the rhetorical and framing strategies of Donald Trump and Joe Biden and to find how they persuade and manipulate audiences of the political discourse. The findings reveal that the rhetorical strategy, Pathos, or the appeal to emotions, was utilized the most by Donald Trump compared to a more balanced use of all rhetorical strategies by Joe Biden. At the same time, logos were focused more on debates for both candidates. For framing strategies, both candidates utilized emphasis the most to stress the importance of an issue or to concentrate on a message. According to the analysis, Donald Trump mainly used persuasive language, which can be seen by referencing "patriots" several times to evoke a sense of unity, which is different compared to his debates, where he is very manipulative in using statistics and facts that are not fully referenced and an abundance of straight lies. This can be seen when he claimed that he cut "drug prices," which is false. Joe Biden used a persuasive strategy by referencing history and events such as "Charlottesville".

This study investigates whether assault frequency increased on days and in cities where candidates Donald Trump and Hillary Clinton held campaign rallies prior to the 2016 US Presidential election. We calculated city-level counts of police-reported assaults for 31 rallies for Donald Trump and 38 rallies for Hillary Clinton. Negative binomial models estimated the assault incidence on rally days (day 0) relative to that on eight control days for the same city (days -28, -21, -14, -7, +7, +14, +21, and +28). Cities experienced an increase in assaults (incidence rate ratio [IRR] = 1.12, 95% CI: 1.03-1.22) on the days of Donald Trump's rallies, and no change in assaults on the days of Hillary Clinton's rallies (IRR = 1.00; 95% CI: 0.94-1.06). Assaults increased on days when cities hosted Donald Trump's rallies during the 2016 Presidential election campaign.

The US presidential election in 2016 drew the attention of the whole world. Both Donald Trump and Hillary Clinton were supported by many people. The news had become a hot topic of Sina microblog (Weibo) for many times. Weibo is an important platform for Chinese public opinion, so the election is valuable for public opinion analysis. Using empirical method, I crawled 21608 search result of Weibo from August 2016 to February 2017 which took “US presidential election” as the keyword. And I used parallel LDA (Latent Dirichlet Allocation) model and software MALLET to make subject analysis about the election after data preprocessing. Then I got 10 topics using LDA and tested the clustering effect utilizing human check method. At last, I found the hot topics by analyzing the clustering result. Topics include real-time report of election, politics, foreign affairs, economics, joke about the vote, tip-off of hackers, Trumps’s victory, dramatic election and derivative news. It reveals Chinese people’s focus which can help improving civic literacy.

The present study analyzed Engagement strategies used in political discourse, examining how politicians construe certain stances relative to other voices and how they align or dis-align with their audience. To this end, the study employed Martin and White’s (2005) appraisal theory to examine the Engagement resources utilized in speeches delivered by the 46th US President Joe Biden and the 45th and 47th President Donald Trump during their campaigns for the 2024 US presidential election during their campaigns for the 2024 US presidential election. Instances of Engagement were identified, tallied, and classified into their respective sub-categories. The findings showed a consistent pattern in terms of the frequencies of the sub-categories of Engagement in the two speeches, with Heteroglossia being more frequent than Monoglossia. Results of the normalized frequencies indicated that contracted and expanded resources were more often employed by Trump than by Biden. However, a fine-grained analysis underscored distinct stylistic differences in the utilization of these resources by the two speakers. The study sheds light on the nuanced nature of political discourse and provides insights into how politicians use Engagement resources to construe certain stances and to communicate with their audience

The 2020 US presidential election is still more than a year away, but the media is noisy due to the continuous registration of candidates that will face Trump in the election. Trump has already started to check is rivals through media. So far, Joe Biden and Bernie Sanders seem to have to most possibility to face Trump in the election. Sensitivity analysis was conducted to the data collected from Twitter from the year 2019. The positivity scores have been proved to effect approval ratings, they are estimated to effect the likeliness of becoming the most popular candidate. The data was compared to the past election from 2008, 2012, and 2016. The elections included the past rival background of Obama and McCain, Obama and Romney, Trump and Clinton to show how positive ratings effect the election. Tweets were collected through HTML and Python. The collected data was analyzed using SPSS and MS Excel. Data was defined into three major statuses; positive, negative, and neutral by a lexicon named Valence Aware Dictionary and Sediment Reasoner (VADER). The null hypothesis was rejected through Independent Sample T-Test, Mann-Whitney U Test, Kruskal Wallis Test to show the difference between means. Research results show who will become Trump's estimated competitor for the 2020 election.

US presidential election in 2016 had brought Donald Trump to the oval office. Compare to the regime before, Trump would make US approaches to its counterparts more hard and firm. In this context, Trump would possibly raise the conflict tense towards China and Islamic World since the US would tend to utilise hard rather than soft power. Trump never pictured clearly his foreign policy direction, but he indicated to prefer isolationism and protectionism. He did not have foreign policy record so that US foreign policy would be very relying on Trump’s rhetorical campaigns. The eccentric leadership of US President Donald Trump is a unique global phenomena which very interesting to be discussed.

In this paper a network-oriented computational model is presented for voting intentions over time specifically for the race between Donald Trump and Hillary Clinton in the 2016 US presidential election. The focus was on the role of social and mass communication media and the statements made by Donald Trump or Hillary Clinton during their speeches. The aim was to investigate the influence on the voting intentions and the final voting. Sentiment analysis was performed to check whether the statements were high or low in language intensity. Simulation experiments using parameter tuning were compared to real world data (3 election polls until the 8th of November).

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Analysis of political discourse on twitter in the context of the 2016 US presidential elections