As of December 2022, X/Twitter's audience accounted for over 368 million monthly active users worldwide. This figure was projected to decrease to approximately 335 million by 2024, a decline of around five percent compared to 2022.

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.

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

This is a data set of 482,251 public tweets and retweets (Twitter IDs) posted by the #edchat online community of educators who discuss current trends in teaching with technology. The data set was collected via Twitter's Streaming API between Feb 1, 2018 and Apr 4, 2018, and was used as part of the research on developing a learning analytics dashboard for teaching and learning with Twitter. Following Twitter's terms of service, the data set only includes unique identifiers of relevant tweets. To collect the actual tweets that are part of this data set, you will need to use one of the available third party tools such as Hydrator or Twarc ("hydrate" function). As part of this release, we are also attaching an enriched version of this data set that contains sentiment and opinion analysis labels that were produced by analyzing each tweet with the help of the NLTK SentimentAnalyzer Python package. *This work was supported in part by eCampusOntario and The Social Sciences and Humanities Research Council of Canada.

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.

This study consists of quantitative, explanatory, and non-experimental research using inductive inference longitudinally. Thus, the use of hashtags by the Twitter accounts of the set of museums that are part of REMED is studied, and the analysis of hashtag trends by Twitter users in Spanish is performed.The primary variable is the favorite count, and it is hypothesized from this study that it is possible to predict the primary variable five weeks later. The field of study is formed by the 104 Twitter accounts of the museums that are part of REMED (Red de Museos y Estrategias Digitales).Seven analysis variables explain the information related to the use of hashtags, both in the size of the Twitter accounts of museums of the sample chosen (prefix "m_" in the variables) and Twitter users in Spanish in general (prefix "tw_" in variables). All variables represent the data in count mode, which means that they sum up the total of the data collected for each tweet of each hashtag processed:Number of tweets (variable name "num_tweets")Number of retweets (variable name "retweet_count") Number of favorites (variable name "favorite_count")Number of followers of tweeters (variable name "user_num_followers")Number of tweets published by tweeters (variable name "user_num_tweets")Age in days of tweeters' Twitter accounts (variable name "user_age")Number of tweets including a URL (variable name "url_inclusion")With the variables above, an investigation has been carried out by checking the correlations between the variables and performing a regression analysis. Thus, the relationships between the variables are ascertained and analyzed to determine if it is possible to predict the number of favorites of the hashtags used by museums. The first initial intake is presented in the file cimed-2021-ini.csv, and the intake made 5 weeks later is presented in the file cimed-2021-end.csv.This dataset has contributed to the elaboration of the book chapter:Yeste Moreno, V.; Calduch-Losa, Á.; Serrano-Cobos, J. (2022). Estudio predictivo del uso colectivo de hashtags en museos de la red REMED. En CIMED21 - I Congreso internacional de museos y estrategias digitales. Editorial Universitat Politècnica de València. 251-265. https://doi.org/10.4995/CIMED21.2021.12281

Demographic data prediction is powered by Demografy AI that extracts demographic data from names with 100% coverage, accuracy preview before purchase and GDPR-compliance.

Demografy is a privacy by design customer demographics prediction AI platform.

Use cases: - Social Media analytics and user segmentation - Competitor analysis - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You need only names of social media users. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in an online media and the possible prediction of the selected success variables.

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

One of the biggest advantages of Twitter is the speed at which information can be passed around. People use Twitter primarily to get news and for entertainment. This is the breakdown of why people use Twitter today.

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

Descriptive statistics and correlations for London data (N = 3,572).

This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021.

The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images.

1. clef_en and clef_ar are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 Barrón-Cedeno et al. ^[1].
2. lesa is an English Twitter dataset for claim detection released by Gupta et al.^[2]
3. mediaeval is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by Pogorelov et al.^[3]

The dataset details like data curation and annotation process can be found in the cited papers.

Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows:
1. clef_en: 281
2. clef_ar: 2571
3. lesa: 1395
4. mediaeval: 1724

Each folder has two sub-folders and a json file data.json that consists of crawled tweets. Two sub-folders are:
1. images: This Contains crawled images with the same name as tweet-id in data.json.
2. splits: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns

Code for the paper: https://github.com/cleopatra-itn/image_text_claim_detection

If you find the dataset and the paper useful, please cite our paper and the corresponding dataset papers^[1,2,3]
Cheema, Gullal S., et al. "On the Role of Images for Analyzing Claims in Social Media" 2^nd International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA) co-located with The Web Conf 2021.

[1] Barrón-Cedeno, Alberto, et al. "Overview of CheckThat! 2020: Automatic identification and verification of claims in social media." International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Cham, 2020.
[2] Gupta, Shreya, et al. "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content." arXiv preprint arXiv:2101.11891 (2021).
[3] Pogorelov, Konstantin, et al. "FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020." MediaEval 2020 Workshop. 2020.

Usecase/Applications possible with the data:

Customer feedback analysis: Analyzing customer feedback can be helpful for businesses to keep customers happy, stay loyal to the brand, and identify any areas to improve.

Social media monitoring: With sentiment analysis, companies can monitor what's being said about them on social media and use that to figure out how people feel about their products and services and track any new trends.

Market research: Sentiment analysis can be used to analyze market trends and consumer preferences, which can help companies make informed business decisions and develop effective marketing strategies.

Financial analysis: You can use sentiment analysis to determine what people say about the stock market through news and social media, which can help you make investing decisions.

For e-commerce (amazon/Bestbuy/home depot and much more) following data fields can be included: Title Price Vendor Name Ratings Reviews Brand ASIN URL Sentiment analysis for each review And other fields, as per request

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 from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007AbstractThe exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Descriptive statistics and correlations for the Los Angeles data (N = 7,838).

As of February 2024, Twitter is ranked as the 12h most popular social media site in the world. The platform currently has 436 million active monthly users.

Our cleaned dataset with id strings of tweets containing "shein" with only original, English tweets for our Winter 2023 Digital Humanities 120: Social Media Data Analytics project at UCLA.

This is the breakdown of Twitter users by age group.

Release of DWP Monthly statistics for Early Estimates for Income Support Lone Parents and Employment and Support Allowance and incapacity benefit client group (ESA/IB).

Source agency: Work and Pensions

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: SS