In 2020, 189 countries were represented through an official presence on Twitter, either by personal or institutional accounts run by heads of state and government and foreign ministers. During the measured period, U.S. President Donald Trump was ranked first, having accumulated over 81.1 million Twitter followers on his personal account. The official @POTUS account was ranked fifth with 30.2 million followers worldwide. Heads of state on Twitter Twitter is a very conversational social platform, allowing users to communicate in a very public manner. Foreign ministries utilize Twitter to expand their online presence and digital diplomatic networks, and government officials are encouraged to interact with the public. The most conversational world leader on Twitter is the Government of Nepal, with 96 percent of their tweets being @ replies to other Twitter users. Another more subtle layer of Twitter diplomacy is the mutual following of peers between official heads of state, minister and other government accounts – as of June 2020, the Foreign Ministry of Iceland (@MFAIceland) was ranked first, having 147 mutual connections with other world leaders and foreign ministries on Twitter. During the measured period, @realDonaldTrump, @POTUS and the @WhiteHouse Twitter accounts did not follow any other foreign leaders. In 2018, the account of the U.S. State Department had only 59 mutual peer connections on Twitter, painting a relatively isolated picture in terms of international political communications. Trump on Twitter Donald Trump’s prolific Twitter usage is a hotly debated topic. The President uses Twitter on a daily basis to make comments about other politicians, celebrities and daily news, sometimes antagonizing others with his controversial statements. According to an August 2018 survey, 61 percent of U.S. adults stated that Trump's use of Twitter as President of the United States was inappropriate, while only 24 percent of respondents said the opposite. In total, 90 percent of respondents who identified as Democrats thought that Trump's Twitter use was inappropriate; while on the other end of the political spectrum only 35 percent of respondents identifying as Republicans reported having the same opinion.

As of January 20, 2023, former United States president Donald J. Trump had approximately 87.73 million followers on his Twitter accounts, registering his biggest audience across all social media platforms. Trump's profiles on Facebook and Instagram followed with 34.49 million and 23.3 million followers each. Meanwhile, his profile on his own platform Truth Social, created after he was banned from mainstream social networks at the beginning of 2021 due to inciting violence, amassed an audience of around 4.83 million followers.

Trump’s own vessel adrift

Although similar alt-tech platforms like Gab and Rumble already existed, the ban of Donald Trump from mainstream social media and the creation of his own network Truth Social were significant but brief boosts for the proliferation of alternative social platforms, which started targeting users who felt displaced or were also banned from traditional platforms. Even though receiving moderate attention during its launch, Truth Social is currently at its lowest popularity so far, with even less relevance in the public debate.

The sprawl of alt-techs

Focused on providing spaces for right-wing publics and their respective discussions, alt-tech platforms have so far only managed to gather niche audiences and limited reach. Parler’s unique monthly visitors shrank from 12.3 million in January 2021 to around 137 thousand in August 2022. Founded by Trump’s former political advisor Jason Miller, Gettr only had mild success at its start in the United States, although recently amassing more followers in countries like Brazil, especially due to its usage by supporters of former president Jair M. Bolsonaro during its last federal elections’ campaign.

This is an Excel workbook containing two sheets. The first sheet contains 503 rows corresponding to 503 Tweet id strings from_user_id_str 25073877 and the following corresponding metadata:created_at time user_lang in_reply_to_user_id_str f from_user_id_str in_reply_to_status_id_str source user_followers_count user_friends_countTweet texts, URLs and other metadata such as profile_image_url, status_url and entities_str have not been included.An attempt to remove duplicated entries was made but duplicates might have remained so further data refining might be required prior to analyses.The second sheet contains 400 rows corresponding to the most frequent terms in the dataset's Tweets' texts. The text analysis was performed with the Terms Tool from Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (2017). An edited English stop words list was applied to remove Twitter data specific terms such as t.co, https, user names, etc. The analysed Tweets contained emojis and other special characters; due to character encoding these will be reflected in the terms list as character combinations. Motivations to Share this DataArchived Tweets can provide interesting insights for the study of contemporary history of media, politics, diplomacy, etc. The queried account is a public account widely agreed to be of exceptional national and international public interest. Though they provide public access to tweeted content in real time, Twitter Web and mobile clients are not suited for appropriate Tweet corpus analysis. For anyone researching social media, access to the data is absolutely essential in order to perform, review and reproduce studies. Archiving Tweets of public interest due to their historic significance is a means to both preserve and enable reproducible study of this form of rapid online communication that otherwise can very likely become unretrievable as time passes. Due to Twitter's current business model and API limits, to date collecting in real time is the only relatively reliable method to archive Tweets at a small scale.So far Twitter data analysis and visualisation has been done without researchers providing access to the source data that would allow reproducibility. It is appreciated that an Excel workbook is far from ideal as a file format, but due to the small scale the intention is to make this data human readable and available to researchers in a variety of non-technical fields. Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using a Python script. The data collection search query was from:realdonaldtrump. A trigger was scheduled to collect atuomatically every hour, this means that any Tweets immediately deleted after publication have not been collected. The original data harvesting was refined to delete duplications, to subscribe to Twitter's Terms and Conditions and so that the data was sorted in chronological order.Duplication of data due to the automated collection is possible so further data refining might be required. The file may not contain data from Tweets deleted by the queried user account immediately after original publication. Both research and experience show that the Twitter search API is not 100% reliable. (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet posted by the queried account during the indicated period. This file dataset is shared for archival, comparative and indicative educational research purposes only. The content included is from a public Twitter account and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The original Tweets, their contents and associated metadata were published openly on the Web from the queried public account and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. The license on this output applies to the data collection; third-party content should be attributed to the original authors and copyright owners. Please note that usernames, user profile pictures and full text of the Tweets collected have not been included in this file. No private personal information is shared in this dataset. As indicated above this dataset does not contain the text of the Tweets. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road.This dataset is shared to archive, document and encourage open educational research into political activity on Twitter.Other ConsiderationsAll Twitter users agree to Twitter's Privacy and data sharing policies. Social media research remains in its infancy and though work has been done to develop best practices there is yet no agreement on a series of grey areas relating to reseach methodologies including ad hoc social media specific research ethics guidelines for reproducible research. It is understood that public figures Tweet publicly with the conscious intention to have their Tweets publicly accessed and discussed. It is assumed that a public figure Tweeting publicly is of public interest and that such figure, as a Twitter user, has given implicit consent, by agreeing explicitly to Twitter's Terms and Conditions, for their Tweets to be publicly accessed and discussed, including critical analysis, without the need for prior written permission. There is therefore no difference between collecting data and performing data analysis from a public printed or online publication and collecting data and performing data analysis of a dataset containing Twitter data from a public account from a public user in a public role. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time. Reproducibility is considered here a key value for robust and trustworthy research. Different scholarly professional associations like the Modern Language Association recognise Tweets, datasets and other online and digital resources as citeable scholarly outputs.The data contained in the deposited file is otherwise available elsewhere through different methods.

Twitter was an integral part of Donald Trump’s communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.

Context

United States 45th President Donald Trump has used Twitter as no one else. He primarily ran his government from a twitter firehose. Twitter has officially banned his account on January 8th 2021 after a deadly riot at Capitol on January 6th 2021. Twitter cites its World Leaders on Twitter: Principles and Approach as a guide to adhere to for public leaders.

Trump tweets and policies have far reaching effects that one can realize or he would accept to realize himself. Since, twitter is suspended there is no public way to read his past tweets and analyze it for public policy outcome or link it with global issues.

Here we are presenting the complete treasure trove of President Trump's tweet, all 56,572 for the public, data scientists and researchers.

Content

The dataset contains 56,572 tweets, tweet IDs, Tweet Date, How many liked and retweeted it.

Acknowledgements

I like to acknowledge Twitter and Trump's Tweet Archives on the Internet that have helped me create this dataset

Inspiration

I’d like to call the attention of my fellow Kagglers and Data Scientists to use Machine Learning and Data Sciences to help me explore these ideas:

• How many times Trump discussed a particular country in his tweets and if we can label the sentiments? (North Korea, India, Pakistan, Mexico?) • How many times Trump talks about immigrants and border wall? • How many times and ways he has insulted? • Can you find a link between his tweets and stock market prices? • How many times he has downplayed Corona/Covid? • How many times he has called the election fraud? • How many tweets about Hillary Clinton, Obama or Joe Biden? • Anything else you can find that surprises us?

Elon Musk officially became the CEO of Twitter on October 27th, 2022. From that week, the follower counts of popular politicians in the United States began to dramatically change. During the week of October 24th to 31st, Senator Ted Cruz of Texas gained 75,977 new followers. Comparatively, Senator Elizabeth Warren of Massachusetts lost 36,902 followers.

Let that sink in The acquisition of Twitter by tech billionaire Elon Musk was finalized on October 27th, 2022. Since then, Musk has made weekly headlines for various changes to the platform such as the “Twitter Blue” verification system, the cutting hundreds of jobs, installing beds in the office, and granting “general amnesty” for all banned accounts including those of former president Donald Trump and Representative Marjorie Taylor Greene.

Fluctuations in the number of Twitter followers of any one account are known to occur. These fluctuations are usually the result of significant social events in wider society or the mass removal of bots from the website and occur in short bursts. However, a clear and prolonged pattern emerged with high profile U.S. politicians in the weeks following the Musk takeover.

Republicans are the biggest winners Republican representatives have seen the largest follower gains in stark contrast to their Democratic and more left-leaning counterparts. It is difficult to know exactly why the post-Musk acquisition fluctuations have occurred, but news outlets have speculated a mass exodus of more liberally minded users, as well as an influx of right-wing bots. Additionally, Musk vocalized his support for the Republican party during the 2022 midterm elections and called for the prosecution of public health official, Anthony Fauci.

On November 2nd, Musk tweeted a picture of a sweater sold by the campaign of Democratic Congresswoman Alexandria Ocasio-Cortez, in which he had circled the price. This tweet gathered more than 400,000 thousand likes and prompted a response from Representative Ocasio-Cortez in which she criticized the Twitter CEO for his treatment of his employees.

This dataset consists of tweet identifiers for tweets harvested between November 28, 2016, following the election of Donald Trump through the end of the first 100 days of his administration. Data collection ended May 1, 2017.

Tweets were harvested using multiple methods described below. The total dataset consists of 218,273,152 tweets. Because of the different methods used to harvest tweets, there may be some duplication.

Methods Data were harvested from the Twitter API using the following endpoints:

search
timeline
filter

Three tweet sets were harvested using the search endpoint, which returns tweets that include a specific search term, user mention, hashtag, etc. The table below provides the search term, data collection dates, the total number of tweets in the corresponding tweet set, and the total number of unique Twitter users represented.

    Search term 
    Dates collected
    Count tweets
    Count unique users




    @realDonaldTrump user mention
    2016-11-28 - 2017-05-01
    4,597,326 
    1,501,806


    "Trump" in tweet text 
    2017-01-18 - 2017-05-01
    11,055,772 
    2,648,849


    #MAGA hashtag 
    2017-01-23 - 2017-05-01
    1,169,897 
    236,033

Two tweet sets were harvested using the timeline endpoint, which returns tweets published by specific users. The table below provides the user whose timeline was harvested, data collection dates, the total number of tweets in the corresponding tweet set, and the total number of unique Twitter users represented. Note that in these cases, tweets were necessarily limited to the one unique user whose tweets were harvested.

    User
    Dates collected
    Count tweets
    Count unique users




    realDonaldTrump
    2016-12-21 - 2017-05-01
    902
    1


    trumpRegrets
    2017-01-15 - 2017-05-01
    1,751
    1

The largest tweet set was harvested using the filter endpoint, which allows for streaming data access in near real time. Requests made to this API can be filtered to include tweets that meet specific criteria. The table below provides the filters used, data collection dates, the total number of tweets in the corresponding tweet set, and the total number of unique Twitter users represented.

Filtering via the API uses a default "OR," so the tweets included in this set satisfied any of the filter terms.

The script used to harvest streaming data from the filter API was built using the Python tweepy library.

    Filter terms
    Dates collected
    Count tweets
    Count unique users



    tweets by realDonaldTrump

    tweet mentions @realDonaldTrump

    'maga' in text

    'trump' in text

    'potus' in text

    2017-01-26 - 2017-05-01
    201,447,504 
    12,489,255

Harvested tweets, including all corresponding metadata, were stored in individual JSON files (one file per tweet).

Data Processing: Conversion to CSV format

Per the terms of Twitter's developer API, tweet datasets may be shared for academic research use. Sharing tweet data is limited to sharing the identifiers of tweets, which must be re-harvested to account for deletions and/or modifications of individual tweets. It is not permitted to share the originally harvested tweets in JSON format.

Tweet identifiers have been extracted from the JSON data and saved as plain text CSV files. The CSV files all have a single column:

id_str (string): A tweet identifier

The data include one tweet identifier per row.

Context

This data contains all of Trump's non-deleted, and non-retweeted Tweets from the day he announced his candidacy for President in 2015 until September 27th, 2018. I utilized this data to create models to predict the number of favorites any given tweet would get based on the content of the messages and information concerning his twitter account.

Acknowledgements

This tweet level was collected from www.trumptwitterarchive.com, and the account level data from www.trackalytics.com.

Inspiration

I wanted these models to predict the number of favorites a tweet got without already knowing how many retweets it got. I managed to produce a model that had a mean absolute error of around 19,500 using NLP techniques and some general knowledge of Trump's behavior. I would love to see others beat my models and create amazing predictors themselves.

As of October 2025, social network X (formerly known as Twitter) was most popular in the United States, with an audience reach of approximately 99.04 million users. Japan ranked second, recording more than 71 million users on the platform. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

According to a June 2020 survey, more than two thirds of U.S. adults who had graduated college felt that Donald Trump tweeted too much. The same was true for 59 percent of respondents whose educational background was some high school or less. The U.S. president is a prolific Twitter user with over 80 million followers on the social media platform. However, Trump's social media usage is often controversial and frequently sparks online and offline debate.

*** Newly Emerged Rumors in Twitter ***

These 12 datasets are the results of an empirical study on the spreading process of newly emerged rumors in Twitter. Newly emerged rumors are those rumors whose rise and fall happen in a short period of time, in contrast to the long standing rumors. Particularly, we have focused on those newly emerged rumors which have given rise to an anti-rumor spreading simultaneously against them. The story of each rumor is as follow :

1- Dataset_R1 : The National Football League team in Washington D.C. changed its name to Redhawks.

2- Dataset_R2 : A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

3- Dataset_R3 : Facebook CEO Mark Zuckerberg bought a "super-yacht" for $150 million.

4- Dataset_R4 : Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

5- Dataset_R5 : Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

6- Dataset_R6 : Harley-Davidson's chief executive officer Matthew Levatich called President Trump "a moron."

7- Dataset_R7 : The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

8- Dataset_R8 : Michael Jordan resigned from the board at Nike and took his Air Jordan line of apparel with him.

9- Dataset_R9 : In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

10- Dataset_R10 : During confirmation hearings for Supreme Court nominee Brett Kavanaugh, congressional Democrats demanded that the nominee undergo DNA testing to prove he is not Adolf Hitler.

11- Dataset_R11 : Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.

12- Dataset_R12 : A screenshot from MyLife.com confirms that mail bomb suspect Cesar Sayoc was registered as a Democrat.

The structure of excel files for each dataset is as follow :

• Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet :

• User ID (user who has posted the current tweet/retweet)

• The description sentence in the profile of the user who has published the tweet/retweet

• The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

• Date and time of creation of the the account by which the current tweet/retweet has been posted

• Language of the tweet/retweet

• Number of followers

• Number of followings (friends)

• Date and time of posting the current tweet/retweet

• Number of like (favorite) the current tweet had been acquired before crawling it

• Number of times the current tweet had been retweeted before crawling it

• Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

• The source (OS) of device by which the current tweet/retweet was posted

• Tweet/Retweet ID

• Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

• Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

• Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

• Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

• State of the tweet which can be one of the following forms (achieved by an agreement between the annotators) :

   r : The tweet/retweet is a rumor post
  
   a : The tweet/retweet is an anti-rumor post
  
   q : The tweet/retweet is a question about the rumor, however neither confirm nor deny it
  
   n : The tweet/retweet is not related to the rumor (even though it contains the queries related to the rumor, but does not refer           to the rumor)
  

Context

The former US president Donald Trump was notoriously active on Twitter. On January 8th, 2021, the platform decided to suspend his account, citing "the risk of further incitement of violence" following the violent riots at the US Capitol building on Jan 6th. Trump's Twitter activity constitutes an important documentation of escalating polarisation in the US political and societal discourse during the second decade of the 2000s.

Content

This dataset contains all of Trump's tweets since 2009. It was copied in its entirety from the website The Trump Archive who did all the work in periodically scraping Trump's Twitter account until his suspension in 2021. All I added was some light cleaning of column names and some equally light text formatting adjustments.

There are several other Trump Tweet datasets on Kaggle, but I didn't see one that was as complete or recent as this archive.

On the completeness of the archive, the website FAQ notes that "the site launched in September 2016. If [Trump] deleted a tweet before that, it won't be in here. If he deleted a tweet since then, it should be in here."

Acknowledgements

All the credit goes to Brendan from The Trump Archive who compiled this data and made it publicly available.

Inspiration

This data should show clearly how Trump's communication focus was shifting over the time, and how preoccupied he was with certain topics; for instance his efforts in overturning the 2020 election results. I'd like to see some visualisations of those shifts in focus.

Context

@realDonaldTrump on Twitter.

Content

The file realDonaldTrumpinoffice.csv contains ALL tweets, including later-deleted tweets, sent by @realDonaldTrump while Donald Trump is in office as the 45th president of the United States (from 20 Jan 2017 to 19 Jan 2021).

The file realDonaldTrumpbfoffice.csv contains tweets sent by @realDonaldTrump before Donald Trump took office.

Acknowledgements

Courtesy of @realDonaldTrump

Inspiration

Twitter has permanently suspended the account @realDonaldTrump on 08 Jan 2021, so the last tweet captured in this dataset is on 08 Jan 2021.

According to a June 2020 survey, ** percent of adults in the United States who identified as Democrats felt that Donald Trump tweeted too much. In contrast, only ** percent of self-identified Republican respondents felt the same and additionally, ** percent of Republican survey respondents stated that Trump tweeted the right amount. The U.S. president is a prolific Twitter user with over ** million followers on the social media platform. However, Trump's social media usage is often controversial and frequently sparks online and offline debate.

[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.

How does Truth Social compare to other social media platforms? There are around 2 million active Truth Social users.

Content

Day-by-Day Data on Donald Trump Tweets till 8th Jan 2021, before blocking of his twitter account. The data contains information on number of retweets, deletion of tweets, device through which tweeted, flagged tweets, favorite tweets, etc.

Collection of tweets related to the 2016 US election that went viral between election day (Nov 8th) and March 2017. Viral tweets are those that achieved the 1000-retweet threshold during the collection period. We queried Twitter's streaming API using the hashtags #MyVote2016, #ElectionDay, #electionnight, and the user handles @realDonaldTrump and @HillaryClinton.

Tweets have been labelled as containing fake news or not by two sets of people. A fake news is one the following:

• Serious fabrication
• Large-scale hoaxes
• Jokes taken at face value
• Slanted reporting of real facts
• Stories where the 'truth' is contentious

During the beginning of the launch, they had some pretty fast growth. Here are the key Truth Social statistics you need to know.