This study investigated the cognitive processing of true and false political information. Specifically, it examined the impact of source credibility on the assessment of veracity when information comes from a polarizing source (Experiment 1), and effectiveness of explanations when they come from one's own political party or an opposition party (Experiment 2). These experiments were conducted prior to the 2016 Presidential election. Participants rated their belief in factual and incorrect statements that President Trump made on the campaign trail; facts were subsequently affirmed and misinformation retracted. Participants then re-rated their belief immediately or after a delay. Experiment 1 found that (i) if information was attributed to Trump, Republican supporters of Trump believed it more than if it was presented without attribution, whereas the opposite was true for Democrats and (ii) although Trump supporters reduced their belief in misinformation items following a correction, they did not change their voting preferences. Experiment 2 revealed that the explanation's source had relatively little impact, and belief updating was more influenced by perceived credibility of the individual initially purporting the information. These findings suggest that people use political figures as a heuristic to guide evaluation of what is true or false, yet do not necessarily insist on veracity as a prerequisite for supporting political candidates.

This statistic shows public opinion on whether it was appropriate for the New York Times to publish data on Donald Trump's tax returns in the United States as of May 2019, sorted by political affiliation. The survey results revealed that ** percent of Democrats thought that it was appropriate for the New York Times to publish data on Donald Trump's tax returns, compared to just ** percent of Republicans who said the same.

Dataset

This dataset was created by MC

Contents

It contains the following files:

The title of this research is "Lexical Cohesion Used in Donald Trump's Campaign Speeches". Lexical cohesion is one of the most important tools for bringing text together. Lexical cohesion is grouped into five types. Due to the large number of types, research on lexical cohesion needs to be carried out and the problems to be studied are: the types and uses of the most common types found in Donald Trump's campaign speeches. The theory used is the theory of lexical cohesion types taken from Cohesion in English by Halliday and Hassan (1976). This study uses four of Donald Trump's speeches as data sources. Data collection is carried out in the form of library research, which searches for and downloads data sources and then reads the relevant data included in it. All data is grouped into the appropriate type group. The data that has been collected is analyzed descriptively and frequency. The results of the study show that five types of lexical cohesion are found in Donald Trump's campaign speeches. The five types of lexical cohesion found are repetition, synonym, superordinate, general words, and collocation. The mostly type of lexical cohesion found is repetition.

The above data set is about the tenure of trump as president of USA and how netizens make classification.

Replication Data and Do-File for "Supporters and Opponents of Donald Trump Respond Differently to Racial Cues: An Experimental Analysis"

Time period: June 1, 2020 through Election Day 2020Through its Cross-Platform Election Advertising Transparency Initiative (CREATIVE) funded in part through a grant from the NSF (Award Number 2235006), the Wesleyan Media Project is actively working on identifying and summarizing federal election content from the general election periods in federal cycles (September through Election Day). In late 2020, we received a request from the collaboration between Facebook and academics studying the 2020 election to obtain our classifications of advertising data relevant to the presidential election (defined by mentions of either candidate). We already had September through Election Day content in hand and worked backward to acquire June through August 2020 content relevant to presidential mentions solely in service of this request. The data provided here, which cover the June 1 to Election Day period from 2020, apply many of our methods employed in CREATIVE but precedes many of the refinements that we have made in identifying and classifying federal advertising to the content relevant to presidential ads. In addition, in the provided data we did not conduct a lot of fine tuning just for the presidential race. If we had built methods solely for delivery of presidential advertising in particular, we likely would have chosen different methods. We would like to remind researchers that the earlier (June through August) period contains, for many races, primary advertising rather than general election advertising, and we did not examine carefully the differences between these two.

[READ THIS FIRST! DATASETS FOR Academic/Learning/Non-commercial purpose]

Context

US Election 2020 is very interesting to look into as it is an election in the middle of a pandemic. Me and my teammate created a twitter crawler using Twitter API and Tweepy for my Artificial Intelligence coursework. We chose Donald Trump as a subject of interest as President Trump was known for his twitter interaction.

I decided to deploy my crawler on post-voting day to conduct a sentiment analysis.

Tweet text in this datasets is suitable for Sentiment Analysis usage.

Content

This raw datasets is crawled using Tweepy library and Twitter API. 2500 tweets were gathered per 15 minutes. There are total of 247,500 row of entries and 13 columns, with the total of 3,217,500 cells of data. Data cleaning is needed to perform before doing any analysis.

Datasets date range: 4th November 2020 - 11th November 2020 Tweets with "Trump", "DonalTrump", "realDonalTrump" were capture.

(The User = user of the particular row) username: Twitter User handle accDesc: Description of the user on profile location: Location of the tweet following: Total number of account the user is following followers: Total number of followers of the user totaltweets: Total tweets created of the user usercreated: Date of the user registered his/her Twitter account tweetcreated: Date of the tweet created favouritecount: tweet <3 count (equivalent to like on Facebook) retweetcount: Total tweet's retweet (equivalent to share on Facebook) text: Text body of the tweet tweetsource: Device used to create this tweet hashtags: hashtag of the tweet in JSON format

Acknowledgements and Disclaimers

Banner and thumbnail courtesy of > visuals < from unsplash.com

Much thanks to my teammate Jiacheng Loh and ChenZhen Li for the efforts.

Please do not use this datasets for any malicious attempts, any damage done is not under the responsible of me.

This datasets were gathered for the purpose of learning and not for commercial purposes.

Data were public in the public domain, therefore i assume these data is open for all.

Limitations

Datasets are gathered with at least 15 minutes interval, therefore datecreated distribution is not equal and may not include all tweets created within the date range.

President Trump Job Approval | RealClearPolling

The above is a data set that compares Trump's and Obama's presidency in USA

Is American democracy under threat? The question is more prominent in political debate now than at any time in recent memory. However, it is also too blunt; there is widespread recognition that democracy is multifaceted and that backsliding, when it occurs, tends to be piecemeal. To address these concerns, we provide original data from surveys of political science experts and the public measuring the perceived importance and performance of U.S. democracy on a number of dimensions during the first year and a half of the Trump presidency. We draw on a theory of how politicians may transgress limits on their authority and the conditions under which constraints are self-enforcing. We connect this theory to our survey data in an effort to identify potential areas of agreement – bright lines – among experts and the public about the most important democratic principles and whether they have been violated. Public and expert perceptions often differ on the importance of specific democratic principles. In addition, though our experts perceive substantial democratic erosion, particularly in areas related to checks and balances, polarization between Trump supporters and opponents undermines any social consensus recognizing these violations.

Does President Trump face domestic costs for foreign policy inconsistency? Will co- partisans and opposition-partisans equally punish Donald Trump for issuing flippant international threats and backing down? While the President said he could “stand in the middle of Fifth Avenue and shoot somebody” without losing voters, the literature consistently shows that individuals, regardless of partisanship, disapprove of leaders who jeopardize the country’s reputation for credibility and resolve. Given the atypical nature of the Trump presidency, and the severe partisan polarization surrounding it, we investigate whether the logic of audience costs still applies in the Trump era. Using a unique experiment fielded during the 2016 presidential transition, we show that Republicans and Democrats impose equal audience costs on President Trump. And by varying the leader’s identity, between Donald Trump, Barack Obama, and “The President,” we demonstrate that the public adheres to a non-partisan logic in punishing leaders who renege on threats. Yet, we also find Presidents Trump and Obama can reduce the magnitude of audience costs by justifying backing down as being “in America’s interest.” Even Democrats, despite their doubts of Donald Trump’s credibility, accept such justifications. Our findings encourage further exploration of partisan cues, leader-level attributes, and leader-level reputations.

United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data was reported at 46.000 % in 29 Oct 2024. This stayed constant from the previous number of 46.000 % for 22 Oct 2024. United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data is updated weekly, averaging 43.000 % from May 2023 (Median) to 29 Oct 2024, with 61 observations. The data reached an all-time high of 46.000 % in 29 Oct 2024 and a record low of 38.000 % in 31 Oct 2023. United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data remains active status in CEIC and is reported by YouGov PLC. The data is categorized under Global Database’s United States – Table US.PR004: The Economist YouGov Polls: 2024 Presidential Election (Discontinued). If an election for president were going to be held now and the Democratic nominee was Joe Biden and the Republican nominee was Donald Trump, would you vote for...

tizerk/trump-speak dataset hosted on Hugging Face and contributed by the HF Datasets community

Does president Trump’s use of Twitter affect financial markets? The president frequently mentions companies in his tweets and, as such, tries to gain leverage over their behavior. We analyze the effect of president Trump’s Twitter messages that specifically mention a company name on its stock market returns. We find that tweets from the president which reveal strong negative sentiment are followed by reduced market value of the company mentioned, whereas supportive tweets do not render a significant effect. Our methodology does not allow us to conclude about the exact mechanism behind these findings and can only be used to investigate short-term effects.

English news that mention the "Donald Trump". Crawled date: Oct, 2024. Documents count: 8,037.

Codebook, data, and replication file for "Partisanship in the Trump Era"

Replication Data for: Trump Tweets and Democratic Attitudes: Evidence from a Survey Experiment (Political Research Quarterly)

2024 National: Trump vs. Harris | RealClearPolling

Dataset

This dataset was created by AlanY

Released under CC0: Public Domain

Contents