Author: Víctor Yeste. Universitat Politècnica de Valencia.This work is an exploratory, quantitative, and not experimental study with an inductive inference type and a longitudinal follow-up. It analyzes movie data and tweets published by users using the official Twitter hashtags of movie premieres the week before, the same week, and the week after each release date.The scope of the study is the collection of movies released in February 2022 in the USA, and the object of the study includes them and the tweets that refer to the film in the 3 closest weeks to their premiere dates. The tweets recollected were classified by the week they were published, so they are classified by a time dimension called timepoint. The week before the release date has been designated as timepoint 1, the week of the release date is timepoint 2, and the week immediately afterward is timepoint 3. Another dimension that has been considered is if the movie has domestic production or not, which means that if one of the countries of origin is the United States, the movie is designated as domestic.The chosen variables are organized in two data tables, one for the movies and one for the collected tweets.Variables related to the movies:id: Internal id of the moviename: Title of the moviehashtag: Official hashtag of the moviecountries: List of countries of the movie, separated by a semicolonmpaa: Film ratings system by the Motion Picture Association of America. It is a completely voluntary rating system and ratings have no legal standing. The currently rating systems include G (general audiences), PG (parental guidance suggested), PG-13 (parents strongly cautioned), R (restricted, under 17 requires accompanying parent or adult guardian) and NC-17 (no one 17 and under admitted)(Film Ratings - Motion Picture Association, n.d.)genres: List of genres of the movie, e.g., Action or Thriller, separated by a semicolonrelease_date: Release date of the movie in a format YYYY-MM-DDopening_grosses: Amount of USA dollars that the movie obtained on the opening date (the first week after the release date)opening_theaters: Amount of USA theaters that released the movie on the opening date (the first week after the release date)rating_avg: Average rating of the movieVariables related to the tweets:id: Internal id of the tweetstatus_id: Twitter id of the tweetmovie_id: Internal id of the movietimepoint: Week number related to the movie premiere that the tweet was published on. “1” is the week before the movie release, “2” is the week after the movie release” and “3” is the second week after the movie release.author_id: Twitter id of the author of the tweetcreated_at: Date and time of the tweet, with format “YYYY-MM-DD HH:MM:SS”quote_count: Number of the tweet’s quotesreply_count: Number of the tweet’s repliesretweet_count: Number of the tweet’s retweetslike_count: Number of the tweet’s likessentiment: Sentiment analysis of the tweet’s content with a range from -1 (negative) to 1 (positive)This dataset has contributed to the elaboration of the book chapters:Yeste, Víctor; Calduch-Losa, Ángeles (2022). Genre classification of movie releases in the USA: Exploring data with Twitter hashtags. In Narrativas emergentes para la comunicación digital (pp. 1012-1044). Dykinson, S. L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). Exploratory Twitter hashtag analysis of movie premieres in the USA. In Desafíos audiovisuales de la tecnología y los contenidos en la cultura digital (pp. 169-187). McGraw-Hill Interamericana de España S.L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). ANOVA to study movie premieres in the USA and online conversation on Twitter. The case of rating average using data from official Twitter hashtags. In El mapa y la brújula. Navegando por las metodologías de investigación en comunicación (pp. 151-168). Editorial Fragua.

These are the key Twitter user statistics that you need to know.

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

As of February 2025, 37.5 percent of X’s (formerly Twitter) global audience was aged between 25 and 34 years. The second-largest age group demographic on the platform was represented by users aged between 18 and 24 years, with a share of 32.1 percent. Users aged less than 18 years accounted for two percent of users, while those aged 50 or older accounted for roughly 7.3 percent. X is a male-dominated platform As of January 2024, more than 60 percent of X users were male. Although all mainstream social media platforms tend to have a slightly more male-skewing audience, X stands out above Instagram, Snapchat, TikTok, and Facebook when it comes to user gender demographics. Overall, Pinterest is the only mainstream platform to have a higher share of female users. X Blue for you It is not uncommon for social media users to now have the chance to become subscribers of their chosen online networks for a monthly fee. X Blue is a subscription service from X that gives users special benefits and features. A blue verification mark, edit post functionality, fewer ads, priority ranking in chats, and longer video upload times are some of the perks offered.

These Twitter user statistics will give you the complete story of where Twitter is at today and what the future looks like for the social media company.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 million and 25 million users respectively. 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.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

Between 2021 and 2022, @DanandMitchy topped the list on metaverse-related Twitter content in terms of impact in South Africa, although the account had only ***** followers. Moreover, @ABdeVilliersk had the second-highest impact score, followed by @RobHutchinson8 and @crypto_bitlord7.

In terms of followers, @ABdeVilliersk was the Twitter account with the most followers tweeting on the metaverse in the country. As of 2022, he had *** million followers. Moreover, @News24 and @Boity followed among the metaverse top tweeters with *** million and *** million followers, respectively.

This is the breakdown of Twitter users by age group.

TweetNERD - End to End Entity Linking Benchmark for Tweets

Paper - Video - Neurips Page

This is the dataset described in the paper TweetNERD - End to End Entity Linking Benchmark for Tweets (accepted to Thirty-sixth Conference on Neural Information Processing Systems (Neurips) Datasets and Benchmarks Track).

Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open sourced dataset benchmark for NERD on Tweets and can be used to facilitate research in this area.

TweetNERD dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) LICENSE.

The license only applies to the data files present in this dataset. See Data usage policy below.

Check out more details at https://github.com/twitter-research/TweetNERD

Usage

We provide the dataset split across the following tab seperated files:

• OOD.public.tsv: OOD split of the data in the paper.
• Academic.public.tsv: Academic split of the data described in the paper.
• part_*.public.tsv: Remaining data split into parts in no particular order.

Each file is tab separated and has has the following format:

tweet_id	phrase	start	end	entityId	score
22	twttr	20	25	Q918	3
21	twttr	20	25	Q918	3
1457198399032287235	Diwali	30	38	Q10244	3
1232456079247736833	NO_PHRASE	-1	-1	NO_ENTITY	-1

For tweets which don't have any entity, their column values for phrase, start, end, entityId, score are set NO_PHRASE, -1, -1, NO_ENTITY, -1 respectively.

Description of file columns is as follows:

Column	Type	Missing Value	Description
tweet_id	string		ID of the Tweet
phrase	string	NO_PHRASE	entity phrase
start	int	-1	start offset of the phrase in text using UTF-16BE encoding
end	int	-1	end offset of the phrase in the text using UTF-16BE encoding
entityId	string	NO_ENTITY	Entity ID. If not missing can be NOT FOUND, AMBIGUOUS, or Wikidata ID of format Q{numbers}, e.g. Q918
score	int	-1	Number of annotators who agreed on the phrase, start, end, entityId information

In order to use the dataset you need to utilize the tweet_id column and get the Tweet text using the Twitter API (See Data usage policy section below).

Data stats

Split	Number of Rows	Number unique tweets
OOD	34102	25000
Academic	51685	30119
part_0	11830	10000
part_1	35681	25799
part_2	34256	25000
part_3	36478	25000
part_4	37518	24999
part_5	36626	25000
part_6	34001	24984
part_7	34125	24981
part_8	32556	25000
part_9	32657	25000
part_10	32442	25000
part_11	32033	24972

Data usage policy

Use of this dataset is subject to you obtaining lawful access to the Twitter API, which requires you to agree to the Developer Terms Policies and Agreements.

Please cite the following if you use TweetNERD in your paper:

@dataset{TweetNERD_Zenodo_2022_6617192,
 author    = {Mishra, Shubhanshu and
         Saini, Aman and
         Makki, Raheleh and
         Mehta, Sneha and
         Haghighi, Aria and
         Mollahosseini, Ali},
 title    = {{TweetNERD - End to End Entity Linking Benchmark 
          for Tweets}},
 month    = jun,
 year     = 2022,
 note     = {{Data usage policy Use of this dataset is subject 
          to you obtaining lawful access to the [Twitter
          API](https://developer.twitter.com/en/docs
          /twitter-api), which requires you to agree to the
          [Developer Terms Policies and
          Agreements](https://developer.twitter.com/en
          /developer-terms/).}},
 publisher  = {Zenodo},
 version   = {0.0.0},
 doi     = {10.5281/zenodo.6617192},
 url     = {https://doi.org/10.5281/zenodo.6617192}
}
@inproceedings{TweetNERDNeurips2022,
 author = {Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali},
 booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
 pages = {},
 title = {TweetNERD - End to End Entity Linking Benchmark for Tweets},
 volume = {2},
 year = {2022},
 eprint = {arXiv:2210.08129},
 doi = {10.48550/arXiv.2210.08129}
}

The most liked tweet of all time was posted on American Actor Chadwick Boseman's Twitter account by his family members in August 2020 after his passing. As of January 2023, the post had seven million likes. Ranking second was a tweet by Elon Musk from April 2022, suggesting he would purchase Coca-Cola. Former U.S. President Barack Obama's 2017 tweet which stated "No one is born hating another person because of the color of his skin or his background or his religion..." has gained four million likes and 1.4 million retweets. Swedish environmental activist Greta Thunberg had two top ranking tweets as of January 2023. Ranking forth, a tweet from Thunberg which was published on December 27th 2022, was a response to American-British media personality Andrew Tate. Tate tweeted Greta and requested an email address to which he could submit a list of his collection of cars and each one's corresponding emissions, resulting in Thunberg's comeback gaining almost four million likes within a matter of days.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

The platform is male-dominated with 68.1% of all Twitter users being male. Just 31.9% of Twitter users are female.

Dataset Card for Twitter Dataset: Tesla

  Dataset Summary

This dataset contains all the Tweets regarding #Tesla or #tesla till 12/07/2022 (dd-mm-yyyy). It can be used for sentiment analysis research purpose or used in other NLP tasks or just for fun. It contains 10,000 recent Tweets with the user ID, the hashtags used in the Tweets, and other important features.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/twitter-dataset-tesla.

Twitter is ranked as the 12h most popular social media site in the world. The platform currently has 611 million active monthly users.

The average happiness score of tweets posted in the Ukrainian language from all over the world decreased sharply on February 24, 2022, when Russia invaded Ukraine. Killings of civilians in Bucha in March 2022, whose evidence was found in early April 2022, and the attack on the Olenivka prison on July 29, 2022, were the events that caused the most significant drops in the happiness score of Ukrainian-language tweets over the course of 2022. In 2023, the lowest score was recorded on May 26, 2023, when the Russian Armed Forces allegedly attacked a medical clinic in Dnipro.

TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.

We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:

1. Tweet Id: Long.

2. Username: String. Encrypted for privacy issues.

3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).

4. Followers: Integer.

5. Friends: Integer.

6. Retweets: Integer.

7. Favorites: Integer.

8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".

9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").

10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".

11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".

12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"

To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.

You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .

Starting from the beginning of 2021, the lowest happiness score of English-language tweets around the world was recorded on January 5, 2021, which marked the U.S. Capitol attack. On that day, the happiness score of the words used in Twitter posts in English was measured at 5.73. Each of the words were assigned a score ranging from one (sad) to nine (happy). Other significant drops in the happiness score were recorded on February 24, 2022, when the Russian invasion of Ukraine began, on May 24, 2022, when a mass shooting took place at Robb Elementary School in Uvalde, Texas, U.S., and on June 24, 2022, after the Roe v. Wade decision was overturned by the Supreme Court, having eliminated a constitutional right to abortion in the U.S. The highest happiness score of tweets in English was recorded on Christmas Day, which is mostly celebrated on December 25, both in 2021 and 2022.

The US has the largest number of Twitter users with over a 100 million users. They account for about 16.7% of all Twitter users worldwide.