The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The number of Twitter users in Brazil was forecast to continuously increase between 2024 and 2028 by in total *** million users (+***** percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Context:

There are lots of really cool datasets getting added to Kaggle every day, and as part of my job I want to help people find them. I’ve been tweeting about datasets on my personal Twitter accounts @rctatman and also releasing a weekly newsletter of interesting datasets.

I wanted to know which method was more effective at getting the word out about new datasets: Twitter or the newsletter?

Content:

This dataset contains two .csv files. One has information on the impact of tweets with links to datasets, while the other has information on the impact of the newsletter.

Twitter:

The Twitter .csv has the following information:

• month: The month of the tweet (1-12)
• day: The day of the tweet (1-31)
• hour: The hour of the tweet (1-24)
• impressions: The number of impressions the tweet got
• engagement: The number of total engagements
• clicks: The number of URL clicks

Fridata Newsletter:

The Fridata .csv has the following information:

• date: The Date the newsletter was sent out
• month: The Month the newsletter was sent out (1-12)
• day: The day the newsletter was sent out (1-31)
• # of dataset links: How many links were in the newsletter
• recipients: How many people received the email with the newsletter
• total opens: How many times the newsletter was opened
• unique opens: How many individuals opened the newsletter
• total clicks: The total number of clicks on the newsletter
• unique clicks: (unsure; provided by Tinyletter)
• notes: notes on the newsletter

Acknowledgements:

This dataset was collected by the uploader, Rachael Tatman. It is released here under a CC-BY-SA license.

Inspiration:

• Which format receives more views?
• Which format receives more clicks?
• Which receives more clicks/view?
• What’s the best time of day to send a tweet?

The number of Twitter users in Indonesia was forecast to continuously increase between 2024 and 2028 by in total 1.4 million users (+6.14 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 24.25 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Malaysia and Singapore.

This is the dataset for the article "A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario".

Abstract:

Museums are embracing social technologies in the attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this paper, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help enhancing the message and increasing the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.

Code to run a selection of experiments is available at https://github.com/rmartoglia/predict-twitter-ch

Dataset structure

The dataset contains the dataset used in the experiments of the above research paper. Only the extracted features for the museum tweet threads (and not the message full text) are provided and needed for the analyses.

We selected 23 well known world spread art museums and grouped them into five groups: G1 (museums with at least three million of followers); G2 (museums with more than one million of followers); G3 (museums with more than 400,000 followers); G4 (museums with more that 200,000 followers); G5 (Italian museums). From these museums, we analyzed ca. 40,000 tweets, with a number varying from 5k ca. to 11k ca. for each museum group, depending on the number of museums in each group.

Content features: these are the features that can be drawn form the content of the tweet itself. We further divide such features in the following two categories:

– Countable: these features have a value ranging into different intervals. We take into consideration: the number of hashtags (i.e., words preceded by #) in the tweet, the number of URLs (i.e., links to external resources), the number of images (e.g., photos and graphical emoticons), the number of mentions (i.e., twitter accounts preceded by @), the length of the tweet;

– On-Off : these features have binary values in {0, 1}. We observe whether the tweet has exclamation marks, question marks, person names, place names, organization names, other names. Moreover, we also take into consideration the tweet topic density: assuming that the involved topics correspond to the hashtags mentioned in the text, we define a tweet as dense of topics if the number of hashtags it contains is greater than a given threshold, set to 5. Finally, we observe the tweet sentiment that might be present (positive or negative) or not (neutral).

Context features: these features are not drawn form the content of the tweet itself and might give a larger picture of the context in which the tweet was sent. Namely, we take into consideration the part of the day in which the tweet was sent (morning, afternoon, evening and night respectively from 5:00am to 11:59am, from 12:00pm to 5:59pm, from 6:00pm to 10:59pm and from 11pm to 4:59am), and a boolean feature indicating whether the tweet is a retweet or not.

User features: these features are proper of the user that sent the tweet, and are the same for all the tweets of this user. Namely we consider the name of the museum and the number of followers of the user.

Context

Right now we see that depression is one of the most common problems in our society. Most of the time people are committed suicide only cause of depression. And till now there is no proper lab test way for detecting depression. Generally, doctors are detecting depression by asking some knowledge-base questions. On the other hand, there are a good number of people using social media platforms right now, where they are sharing their daily experiences, emotion, and other activity with their friends. Twitter is one of the common social platforms and also popular for data collection. I was collecting these datasets from twitter based on some depressive words. I hope that this twitter datasets will help researchers to detect depression more precisely.

Content

Raw data from twitter

Acknowledgements

Chowdhury, Sawrav (2020), “Raw Twitter Datasets Based on Depressive Words”, Mendeley Data, V1, doi: 10.17632/4rd637tddf.1

This is data collected throughout 2017 and 2019 which contains one of a selection of hashtags related to Brexit. The data only contains the tweet id's. The data was collected in order to examine what made people forward facts and counter-facts. The tweets were examined to see how often they had been retweeted and on what time scale. We looked at the content of the tweets and analyzed the tweet metadata. We found that who you are is often more important than what you say with tweets from celebrities and verified uses being forward more often. We found that tweets with images were much more likely to be retweeted. We discovered with content related forwards the resonance of an issue was not determined by how many times something was said but by how often something was retweeted and over what period of time.This collaborative project between the Neuropolitics Research Lab (NRlabs), at the University of Edinburgh and Full Fact, the UK's independent fact-checking organization, employs neuroscientific, psychological and behavioural insights to help us to understand what makes Brexit-related claims spread on digital platforms. Using cutting edge scientific techniques in big data analysis this project offers new insights into how citizens' expectations on Brexit and its consequences are shaped in an increasingly digital world. It will inform organisations on how to communicate what is often dry and complex information related to Brexit in a credible, trustworthy and memorable way using digital communications. These insights will be essential for the strategic management, implementation and public communication of the Article 50 process for the UK's withdrawal from the EU. The question of what constitutes a fact (or an alternative fact) has perhaps never been more salient in public debate. The thirst for 'facts' during the Brexit referendum campaign was a key feature of public debate as was the question of whose facts count. The role of experts in the delivery of factual information came under close scrutiny and became a substantive feature of campaign dialogues. The question of trust and authority in information transmission has been under serious challenge. Citizens' expectations of Brexit and its consequences are, at least in part, shaped by their evaluation of the facts - but how do they decide what is a trustworthy fact? What factors lead them to imbue some sources of information with greater authority than others and under what circumstances do they choose to engage with, share or champion certain 'facts'? How does the context in which 'facts' are disseminated shape the expectations of the citizens on Brexit? Digital technology and online communication platforms such as Twitter and Facebook, play an increasingly important role in the public communication of both information and misinformation. To date, however, we have little information on how 'facts' transmitted in these digital platforms are internalized by recipients and on how this information impacts on citizens' expectations. We investigate how membership of a specific social media bubble impacts on the evaluation of the information received; how the status of the sender or even the content of the communication (whether it contains an image or a web link) matters; and how the nature of the information received, confirmatory or challenging of previous knowledge, impacts on fact transmission to different publics. This project builds on the extensive engagement of two research teams on Brexit-related research and with the UK in a Changing Europe team. Both teams are engaged at the highest level in stakeholder engagement and the project is built on a co-production model, ensuring that the issues addressed are of direct interest to those most likely to utilise the insights developed directly in their daily work. The project is designed in close collaboration with stakeholders to ensure that it can adapt swiftly to maintain relevance in the fast-moving Brexit environment. The project has access to a unique social media data-base of over 40 million tweets that NRlabs has collected on the Brexit debate since August 2015; the cutting edge skills and facilities for conducting experimental research at NRlabs; and ensures daily policy relevance through Full Fact's engagement, nationally and internationally, in the fact checking environment. The contribution of this project addresses the very heart of the mission of the UK in a Changing Europe programme - to be the authoritative source for independent research on UK-EU relations, underpinned by scientific excellence and generating and communicating innovative research with real world impact. The data as gathered from the twitter API. Tweets contain one of a variety of hashtags relating to Brexit as defined by a specially selected expert panel.

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset contains historical data on street accidents in Bulgaria and the capital Sofia for each day since 2004 up to this day. This data has been scraped from the Internal Ministry's website and the missing dates were interpolated based on the end-of-month numbers. It only contains capital- and nation-wide stats on accidents and injuries. Not geocoded accidents or individual reports.

Additionally, there's a daily updated dataset with details on crashes in the capital like location, type, reasons and people injured. The addresses of the heavy accidents are geotagged daily with a dedicated crowdsourcing tool with the help of the Bulgarian twitter community.

All the data, including the historical stats and the daily updates are available as SQL database for download.

Original site

Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec http://snap.stanford.edu/data/index.html email jure at cs.stanford.edu

Citation for the SNAP collection:

@misc{snapnets, author = {Jure Leskovec and Andrej Krevl}, title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection}, howpublished = {\url{http://snap.stanford.edu/data}}, month = jun, year = 2014 }

The following matrices/graphs were added to the collection in June 2010 by Tim Davis (problem id and name):

2284 SNAP/soc-Epinions1 who-trusts-whom network of Epinions.com 2285 SNAP/soc-LiveJournal1 LiveJournal social network 2286 SNAP/soc-Slashdot0811 Slashdot social network, Nov 2008 2287 SNAP/soc-Slashdot0902 Slashdot social network, Feb 2009 2288 SNAP/wiki-Vote Wikipedia who-votes-on-whom network 2289 SNAP/email-EuAll Email network from a EU research institution 2290 SNAP/email-Enron Email communication network from Enron 2291 SNAP/wiki-Talk Wikipedia talk (communication) network 2292 SNAP/cit-HepPh Arxiv High Energy Physics paper citation network 2293 SNAP/cit-HepTh Arxiv High Energy Physics paper citation network 2294 SNAP/cit-Patents Citation network among US Patents 2295 SNAP/ca-AstroPh Collaboration network of Arxiv Astro Physics 2296 SNAP/ca-CondMat Collaboration network of Arxiv Condensed Matter 2297 SNAP/ca-GrQc Collaboration network of Arxiv General Relativity 2298 SNAP/ca-HepPh Collaboration network of Arxiv High Energy Physics 2299 SNAP/ca-HepTh Collaboration network of Arxiv High Energy Physics Theory 2300 SNAP/web-BerkStan Web graph of Berkeley and Stanford 2301 SNAP/web-Google Web graph from Google 2302 SNAP/web-NotreDame Web graph of Notre Dame 2303 SNAP/web-Stanford Web graph of Stanford.edu 2304 SNAP/amazon0302 Amazon product co-purchasing network from March 2 2003 2305 SNAP/amazon0312 Amazon product co-purchasing network from March 12 2003 2306 SNAP/amazon0505 Amazon product co-purchasing network from May 5 2003 2307 SNAP/amazon0601 Amazon product co-purchasing network from June 1 2003 2308 SNAP/p2p-Gnutella04 Gnutella peer to peer network from August 4 2002 2309 SNAP/p2p-Gnutella05 Gnutella peer to peer network from August 5 2002 2310 SNAP/p2p-Gnutella06 Gnutella peer to peer network from August 6 2002 2311 SNAP/p2p-Gnutella08 Gnutella peer to peer network from August 8 2002 2312 SNAP/p2p-Gnutella09 Gnutella peer to peer network from August 9 2002 2313 SNAP/p2p-Gnutella24 Gnutella peer to peer network from August 24 2002 2314 SNAP/p2p-Gnutella25 Gnutella peer to peer network from August 25 2002 2315 SNAP/p2p-Gnutella30 Gnutella peer to peer network from August 30 2002 2316 SNAP/p2p-Gnutella31 Gnutella peer to peer network from August 31 2002 2317 SNAP/roadNet-CA Road network of California 2318 SNAP/roadNet-PA Road network of Pennsylvania 2319 SNAP/roadNet-TX Road network of Texas 2320 SNAP/as-735 733 daily instances(graphs) from November 8 1997 to January 2 2000 2321 SNAP/as-Skitter Internet topology graph, from traceroutes run daily in 2005 2322 SNAP/as-caida The CAIDA AS Relationships Datasets, from January 2004 to November 2007 2323 SNAP/Oregon-1 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2324 SNAP/Oregon-2 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2325 SNAP/soc-sign-epinions Epinions signed social network 2326 SNAP/soc-sign-Slashdot081106 Slashdot Zoo signed social network from November 6 2008 2327 SNAP/soc-sign-Slashdot090216 Slashdot Zoo signed social network from February 16 2009 2328 SNAP/soc-sign-Slashdot090221 Slashdot Zoo signed social network from February 21 2009

Then the following problems were added in July 2018. All data and metadata from the SNAP data set was imported into the SuiteSparse Matrix Collection.

2777 SNAP/CollegeMsg Messages on a Facebook-like platform at UC-Irvine 2778 SNAP/com-Amazon Amazon product network 2779 SNAP/com-DBLP DBLP collaboration network 2780 SNAP/com-Friendster Friendster online social network 2781 SNAP/com-LiveJournal LiveJournal online social network 2782 SNAP/com-Orkut Orkut online social network 2783 SNAP/com-Youtube Youtube online social network 2784 SNAP/email-Eu-core E-mail network 2785 SNAP/email-Eu-core-temporal E-mails between users at a research institution 2786 SNAP/higgs-twitter twitter messages re: Higgs boson on 4th July 2012. 2787 SNAP/loc-Brightkite Brightkite location based online social network 2788 SNAP/loc-Gowalla Gowalla location based online social network 2789 SNAP/soc-Pokec Pokec online social network 2790 SNAP/soc-sign-bitcoin-alpha Bitcoin Alpha web of trust network 2791 SNAP/soc-sign-bitcoin-otc Bitcoin OTC web of trust network 2792 SNAP/sx-askubuntu Comments, questions, and answers on Ask Ubuntu 2793 SNAP/sx-mathoverflow Comments, questions, and answers on Math Overflow 2794 SNAP/sx-stackoverflow Comments, questions, and answers on Stack Overflow 2795 SNAP/sx-superuser Comments, questions, and answers on Super User 2796 SNAP/twitter7 A collection of 476 million tweets collected between June-Dec 2009 2797 SNAP/wiki-RfA Wikipedia Requests for Adminship (with text) 2798 SNAP/wiki-talk-temporal Users editing talk pages on Wikipedia 2799 SNAP/wiki-topcats Wikipedia hyperlinks (with communities)

The following 13 graphs/networks were in the SNAP data set in July 2018 but have not yet been imported into the SuiteSparse Matrix Collection. They may be added in the future:

amazon-meta ego-Facebook ego-Gplus ego-Twitter gemsec-Deezer gemsec-Facebook ksc-time-series memetracker9 web-flickr web-Reddit web-RedditPizzaRequests wiki-Elec wiki-meta wikispeedia

The 2010 description of the SNAP data set gave these categories:

• Social networks: online social networks, edges represent interactions between people

• Communication networks: email communication networks with edges representing communication

• Citation networks: nodes represent papers, edges represent citations

• Collaboration networks: nodes represent scientists, edges represent collaborations (co-authoring a paper)

• Web graphs: nodes represent webpages and edges are hyperlinks

• Blog and Memetracker graphs: nodes represent time stamped blog posts, edges are hyperlinks [revised below]

• Amazon networks : nodes represent products and edges link commonly co-purchased products

• Internet networks : nodes represent computers and edges communication

• Road networks : nodes represent intersections and edges roads connecting the intersections

• Autonomous systems : graphs of the internet

• Signed networks : networks with positive and negative edges (friend/foe, trust/distrust)

By July 2018, the following categories had been added:

• Networks with ground-truth communities : ground-truth network communities in social and information networks

• Location-based online social networks : Social networks with geographic check-ins

• Wikipedia networks, articles, and metadata : Talk, editing, voting, and article data from Wikipedia

• Temporal networks : networks where edges have timestamps

• Twitter and Memetracker : Memetracker phrases, links and 467 million Tweets

• Online communities : Data from online communities such as Reddit and Flickr

• Online reviews : Data from online review systems such as BeerAdvocate and Amazon

https://sparse.tamu.edu/SNAP

We aim to collect, clean, and store corpora of Fon and French sentences for Natural Language Processing researches including Neural Machine Translation, Named Entity Recognition, etc. for Fon, a very low-resourced and endangered African native language.

Fon (also called Fongbe) is an African-indigenous language spoken mostly in Benin, Togo, and Nigeria - by about 2 million people.

As training data is crucial to the high performance of a machine learning model, the aim of this project is to compile the largest set of training corpora for the research and design of translation and NLP models involving Fon.

Through crowdsourcing, Google Form Surveys, we gathered and cleaned #25377 parallel Fon-French# all based on daily conversations.

To the crowdsourcing, creation, and cleaning of this version have contributed:

1) Name: Bonaventure DOSSOU
Affiliation: MSc Student in Data Engineering, Jacobs University
Contact: femipancrace.dossou@gmail.com

2) Name: Ricardo AHOUNVLAME
Affiliation: Student in Linguistics
Contact: tontonjars@gmail.com

3) Name: Fabroni YOCLOUNON
Affiliation: Creator of the Label IamYourClounon
Contact: iamyourclounon@gmail.com

4) Name: BeninLangues
Affiliation: BeninLangues
Contact: https://beninlangues.com/

5) Name: Chris Emezue
Affiliation: MSc Student in Mathematics in Data Science, Technical University of Munich
Contact: chris.emezue@gmail.com

_

To join as a contributor, please contact us at:
1) https://twitter.com/bonadossou
2) https://twitter.com/ChrisEmezue
3) https://twitter.com/edAIOfficial
Or contact Bonaventure Dossou (femipancrace.dossou@gmail.com), Chris Emezue (chris.emezue@gmail.com)
_

Clavier Fongbé (WebView): https://bonaventuredossou.github.io/clavierfongbe/ (Made by Bonaventure Dossou)
Clavier Fongbé (Mobile Android Version): https://play.google.com/store/apps/details?id=com.fulbertodev.clavierfongbe&hl=en&gl=US (Fabroni Yoclounon, Bonventure Dossou et. al.)

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.