A December 2022 study revealed that the user base of Twitter is projected to ******* in the upcoming two years. Thus, in 2023 the social network will see a decrease of nearly **** percent, which in 2024 will reach down to **** percent ******** growth of monthly active users.

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.

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

In 2020, Twitter was downloaded approximately ******* times through the Apple App Store and roughly ******* times on the Google Play Store. According to figures provided by Airnow, downloads of the application in the Netherlands decreased.

This is the breakdown of Twitter users by age group.

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

This dataset features information on all the events that were automatically extracted from Twitter and used as input to periodicity detection, as described in the paper:F. Kunneman and A. Van den Bosch (2015), Automatically identifying periodic social events from Twitter, Proceedings of the RANLP 2015 (pp. 320-328), http://hdl.handle.net/2066/143994The paper describes approaches to identifying periodic events in Twitter. This dataset contains the output of these approaches, as well as a human assessment of the quality of a selection of the output. Apart from information on all the events, tweet ids are shared that can be used to query the tweets and all their meta-data from Twitter.

The .xls file deposited here contains an archive of approximately 985 Tweets published publicly by the @epn public Twitter account between 22/01/2014 14:27 GMT and 05/05/2015 13:16 GMT. This dataset does not contain any data that would otherwise not be already publicly available online through the Twitter API and related Web and mobile services and is only shared in spreadsheet form as a means to preserve social media data for legitimate open data research into public activity on Twitter. Please refer to the ReadMe sheet in the file for important context and more information.

This is the data for "Effective tweeting strategies: making 140 characters count". Every line is the ID of a single tweet, which can be used to retrieve the original tweet and its metadata (such as the original poster, the number of retweets, and so on) via Twitter API or third party Twitter data resellers; the metadata retrieved using these IDs can be used to fully replicate our study. We cannot share the metadata directly because according to Twitter's terms and conditions, we can "only distribute or allow download of Tweet IDs and/or User IDs". The data is collected between Nov. 1, 2013 and Apr. 30, 2015 (18 months in total) for 258 accounts (media, companies, investors and CEOs) using Twitter's Streaming API. For every tweet and its retweets, the Tweet ID in this file includes the latest retweet (or if there are no retweets, it is the ID of the original tweet), such that the metadata for the corresponding tweet is the most up-to-date. There are 2,469,642 Tweet IDs enclosed; after purging invalid tweets (e.g., those having invalid timestamps), we use 2,452,120 tweets and the corresponding 121,772,646 user engagements (retweets, favorites, replies) in the paper. Please contact jxu5 at nd dot edu for more info.

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.

Annual fact sheets providing statistics on the Social Security and Supplemental Security Income programs, including the number of people receiving benefits and the amount of total monthly payments, in each state, territory, and Congressional district. Report for 2015.

This dataset features the training models, emotion classifications and emotion patterns before and after events, related to the paper:F. Kunneman, M. van Mulken and A. Van den Bosch, Anticipointment detection in event tweets (under review)Abstract of the study:We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession that is sometimes referred to as anticipointment. The application of computational approaches makes it possible to detect the presence and strength of this hypothetical relation for a large number of events. We extracted events from a longitudinal data set of Dutch Twitter posts, and modeled classifiers to recognize emotion in the tweets related to those events by means of hashtag-labeled training data. After classifying all tweets before and after the events in our data set, we summarized the collective emotions by calculating the percentage of tweets classified with an emotion as well as ranking tweets based on the classifier confidence score for an emotion and selecting the 90th percentile. Only a weak correlation of around 0.2 was found between positive expectation and disappointment, while a higher correlation of 0.6 was found between positiveexpectation and satisfaction. The most anticipointing events were events with a clear loss, such as a canceled event or when the favored sports team had lost. We conclude that senders of Twitter posts might be more inclined to share satisfaction than disappointment after a much anticipated event.Subject period: January 1st 2011 until October 31st 2015 Date: start=2015-11-01; end=2016-02-28 (data collection)

In 2022, the number of active Twitter users in Spain has decreased to 564 thousand, the lowest since 2015. Despite that, the platform was able to increase its total number of profiles in up to 4.39 million.

Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.

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

Annual fact sheets providing statistics on the Social Security and Supplemental Security Income programs, including the number of people receiving benefits and the amount of total monthly payments, in each state, territory, and Congressional district. This expanded edition of Congressional Statistics, 2015 includes data in Table 2 on the number of SSI recipients under age 18. This data will be included in future editions. Expanded report for 2015.

A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service"). You can download the non-aggregated results (55,000 rows) here.

This data set contains a collection of Twitter rumours and non-rumours during six real-world events: 1) 2013 Boston marathon bombings, 2) 2014 Ottawa shooting, 3) 2014 Sydney siege, 4) 2015 Charlie Hebdo Attack, 5) 2014 Ferguson unrest, and 6) 2015 Germanwings plane crash

The data set is an augmented data set of the PHEME dataset of rumours and non-rumours based on two data sets: the PHEME data [2] (downloaded via https://figshare.com/articles/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078), and the CrisisLexT26 data [3] (downloaded via https://github.com/sajao/CrisisLex/tree/master/data/CrisisLexT26/2013_Boston_bombings).

PHEME-Aug v2.0 (aug-rnr-data_filtered.tar.bz2 and aur-rnr-data_full.tar.bz2) contains augmented data for all six events.

aug-rnr-data_full.tar.bz2 contains source tweets and replies without temporal filtering. Please refer to [1] for details about temporal filtering. The statistics are as follows:

* 2013 Boston marathon bombings: 392 rumours and 784 non-rumours

* 2014 Ottawa shooting: 1,047 rumours and 2,072 non-rumours

* 2014 Sydney siege: 1,764 rumours and 3,530 non-rumours

* 2015 Charlie Hebdo Attack: 1,225 rumours and 2,450 non-rumours

* 2014 Ferguson unrest: 737 rumours and 1,476 non-rumours

* 2015 Germanwings plane crash: 502 rumours and 604 non-rumours

aug-rnr-data_filtered.tar.bz2 contains source tweets, replies, and retweets after temporal filtering and deduplication. Please refer to [1] for details. The statistics are as follows:

* 2013 Boston marathon bombings: 323 rumours and 645 non-rumours

* 2014 Ottawa shooting: 713 rumours and 1,420 non-rumours

* 2014 Sydney siege: 1,134 rumours and 2,262 non-rumours

* 2015 Charlie Hebdo Attack: 812 rumours and 1,673 non-rumours

* 2014 Ferguson unrest: 471 rumours and 949 non-rumours

* 2015 Germanwings plane crash: 375 rumours and 402 non-rumours

The data structure follows the format of the PHEME data [2]. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet. Also each folder contains ‘aug_complete.csv’ and ‘reference.csv'.

'aug_complete.csv' file contains the metadata (tweet ID, tweet text, timestamp, and rumour label) of augmented tweets before deduplication and filtering tweets without context (i.e., replies).

'reference.csv' file contains manually annotated reference tweets [2, 3].

If you use our augmented data (PHEME-Aug v2.0), please also cite:

[1] Han S., Gao, J., Ciravegna, F. (2019). "Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection", The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), Vancouver, Canada, 27-30 August, 2019

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[2] Kochkina, E., Liakata, M., & Zubiaga, A. (2018). All-in-one: Multi-task Learning for Rumour Verification. COLING.

[3] Olteanu, A., Vieweg, S., & Castillo, C. (2015, February). What to expect when the unexpected happens: Social media communications across crises. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 994-1009). ACM

CHECK THE OPEN ACCESS VERSION OF THIS DATASET: https://zenodo.org/record/579597

Counts of geolocated tweets containing the word ‘flood’ or ‘banjir’ within the city of Jakarta, Indonesia for the 2014/2015 monsoon season. Counts were created within ‘RW’ municipal areas at hourly intervals, and include confirmed reports of flooding sent by members of the public to the @petajkt twitter account, as well as other unconfirmed tweets which match specified keywords. Keyword matching is based on substring pattern matching, and so can include tweets where a keyword is part of another word or hashtag. Data captured between 01/12/2014 - 31/03/2015