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 research project, SPARTA (Social Media Analysis for Everyone), funded by dtec.bw (which is funded by the European Union – NextGenerationEU), monitors the 2025 German federal election live as it unfolds on TikTok, YouTube and X/Twitter. Since November 7, 2024, the day the "traffic light" coalition collapsed, we have been collecting and analyzing all German-language posts and reposts on X (formerly Twitter) related to the federal elections. Simultaneously, we gather data from TikTok and YouTube, focusing on the accounts of political parties, including those of candidates and current members of the Bundestag, during the same period. Our analysis includes, among other things, the stances expressed towards political parties and leading candidates, the most discussed issues and hashtags, the outreach of political parties across different platforms, the visibility of female candidates, the occurrence of negative campaigning, the rise of toxic language, and the activity of various actors across platforms. We publish the results in real time on our publicly accessible dashboard (https://dtecbw.de/sparta/), which provides interactive and customizable graphics, making it relevant to a broad audience from politics, academia, journalism, and society. To facilitate real-time analysis of the election campaign, we compiled a dataset based on the data of the federal election officer (Bundeswahlleiterin), containing the TikTok, YouTube and X/Twitter handles of all candidates running for a seat in the parliament. This dataset includes the handles as well as additional information about the candidates from eight political parties: AfD, BSW, Buendnis 90/Die Gruenen, CDU, CSU, Die Linke, FDP and SPD.

Description

The MIGR-TWIT Corpus is a multilingual corpus of tweets about the topic of migration in Europe. Within the framework of the collaborative research project OLiNDiNUM (Observatoire LINguistique du DIscours NUMérique, Linguistic Observatory of Online Debate) the MIGR-TWIT Corpus is created with the aim of developing language databases of online debate. Considering the global issue of migration in line with British and French political contexts of last dozen years from 2011 to 2022, the corpus consists of two sub-corpora:

• FR-R-MIGR-TWIT-2011-2022 Corpus for French language data (1 January 2011 - 30 June 2022) and

• UK-R-MIGR-RA-TWIT-2012-2022 Corpus for English language data (1 January 2012 - 5 September 2022)

Using the Twitter API v2 Academic Research, tweets containing at least one occurrence of migration or refugee related words are retrieved automatically from 28 right and far-right political figures and parties. The whole corpus contains 18,233 tweets and 533,198 words.

Scientific reference:

Pietrandrea, P., Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics 192, 139-157.

Blandino, G. (2023). 10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter, MA University of Wolverhampton

Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.

Contents

The whole corpus contains two CSV Zip files (tabular format) corresponding to each sub-corpus. The complete corpus is presented in two versions, one version with the tweet identifier (data_id) and the text of the tweet (data_text) as a header (folders named FR-R-MIGR-TWIT-2011-2022_textonly and UK-R-MIGR-RA-TWIT-2012-2022_textonly, respectively composed of 12 and 11 Zip files of every single year), and the other version with all tweet fields information included as a header, such as the posting date (data_created_at), the username (author_name), the number of retweets (data_public_metrics_retweet_count), etc., with two folders named FR-R-MIGR-TWIT-2011-2022_meta and UK-R-MIGR-RA-TWIT-2012-2022_meta. Detailed information for each sub-corpus is illustrated below.

1. FR-R-MIGR-TWIT-2011-2022

• Created at: 2022-08-08

• Language: FR

• Coverage: 16 user accounts; 11,761 tweets; 358,491 words

• Time of data collection: start=2011-01-01; end=2022-06-30

• Keywords: words derived from a latin root “migr” of migrare

• Corpus composition:

	Political figure/party	Username	Tweets	Year concerned
1	Michel Barnier	@MichelBarnier	31	2017-22
2	Valérie Pécresse	@vpecresse	81	2017-22
3	Rassemblement National	@RNational_off	3,347	2017-22
4	Nicolas Dupont-aignan	@dupontaignan	663	2011-22
5	Éric Ciotti	@ECiotti	1,007	2012-22
6	Christian Estrosi	@cestrosi	137	2011-22
7	Marine Le Pen	@MLP_officiel	1,650	2011-22
8	Valérie Boyer	@valerieboyer13	837	2012-22
9	Florian Philippot	@f_philippot	485	2012-22
10	Xavier Bertrand	@xavierbertrand	70	2017-22
11	Marion Maréchal	@MarionMarechal	479	2012-17,19-22
12	Philippe Meunier	@Meunier_Ph	245	2013-22
13	Jordan Bardella	@J_Bardella	1,095	2013-22
14	Nicolas Bay	@NicolasBay_	1,260	2017-22
15	Emmanuel Macron	@EmmanuelMacron	72	2017-22
16	Éric Zemmour	@ZemmourEric	302	2019-22
17	Jean Messiha*	Banned from Twitter (since July 2021)	-	-

• Political figures and parties of table above are listed in chronological order according to the dates on which they posted their first tweet.

• *Before the launching of Twitter API v2 Academic Research, migr-tweets were collected from the database of Europresse.com including 1,453 tweets of Jean Messiha as part of the reference study (Pietrandrea & Battaglia 2022). However, the Twitter account in question has been permanently banned since July 2021. For our data collection using the Twitter API started in September 2021, we could not access this account. Therefore, we decided not to include his tweets in the FR-R-MIGR-TWIT-2011-2022 for the sake of consistency with the rest of twitter data that are automatically retrieved.

• The sub-corpus FR-R-MIGR-TWIT-2017-2022 is developed, annotated and analyzed as part of a doctoral thesis in progress (Jeon, 2025) with the aim of studying the semantic construction of migr-lexicon over the period between 2011 and 2022.

2. UK-R-MIGR-RA-TWIT-2012-2022

• Created at: 2022-09-06

• Language: EN

• Coverage: 12 user accounts; 6,472 tweets; 174,707 words

• Time of data collection: start=2012-01-01; end=2022-09-05

• Keywords: words derived from a latin root “migr” of migrare in addition to the keywords “refugee(s)” and “asylum”.

• Corpus composition:

	Political figure/party	Username	Tweets	Year concerned
1	David Cameron	@David_Cameron	32	2012-22
2	Amber Rudd	@AmberRuddUK	29	2012-22
3	Sajid Javid	@sajidjavid	84	2012-22
4	Boris johnson	@BorisJohnson	80	2015-22
5	Priti Patel	@pritipatel	304	2012-22
6	UK Home Office	@ukhomeoffice	909	2012-22
7	Nigel Farage	@Nigel_Farage	1,010	2012-22
8	Richard Tice	@TiceRichard	180	2013-22
9	UKIP	@UKIP	2,746	2012-22
10	Neil Hamilton	@NeilUKIP	252	2013-22
11	Nick Griffin	@NickGriffinBU	542	2012-22
12	Robin Tilbrook	@RobinTilbrook	304	2012-22

• 2 out of 12 accounts are official accounts belonging to the” UK Home Office” department and the “UKIP” (United Kingdom Independence Party) party. 10 out of 12 accounts are political figures’ accounts.

• The corpus UK-R-MIGR-RA-TWIT-2012-2022 will be exploited for the following master’s thesis: Blandino, G. (2023). 10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter, MA University of Wolverhampton.

The first public large-scale multilingual Twitter dataset related to the FIFA World Cup 2022, comprising over 28 million posts in 69 unique spoken languages, including Arabic, English, Spanish, French, and many others. This dataset aims to facilitate research in future sentiment analysis, cross-linguistic studies, event-based analytics, meme and hate speech detection, fake news detection, and social manipulation detection.

The file 🚨Qatar22WC.csv🚨 contains tweet-level and user-level metadata for our collected tweets. 🚀Codebook for FIFA World Cup 2022 Twitter Dataset🚀 | Column Name | Description| |-------------------------------- |----------------------------------------------------------------------------------------| | day, month, year | The date where the tweet posted | | hou, min, sec | Hour, minute, and second of tweet timestamp | | age_of_the_user_account | User Account age in days | | tweet_count | Total number of tweets posted by the user | | location | User-defined location field | | follower_count | Number of followers the user has | | following_count | Number of accounts the user is following | | follower_to_Following | Follower-following ratio | | favouite_count | Number of likes the user did| | verified | Boolean indicating if the user is verified (1 = Verified, 0 = Not Verified) | | Avg_tweet_count | Average tweets per day for the user activity| | list_count | Number of lists the user is a member | | Tweet_Id | Tweet ID | | is_reply_tweet | ID of the tweet being replied to (if applicable) | | is_quote | boolean representing if the tweet is a quote | | retid | Retweet ID if it's a retweet; NaN otherwise | | lang | Language of the tweet | | hashtags | The keyword or hashtag used to collect the tweet | | is_image, | Boolean indicating if the tweet associated with image| | is_video | Boolean indicating if the tweet associated with video | |-------------------------------|----------------------------------------------------------------------------------------|

Examples of use case queries are described in the file 🚨fifa_wc_qatar22_examples_of_use_case_queries.ipynb🚨 and accessible via: https://github.com/khairied/Qata_FIFA_World_Cup_22

🚀 Please Cite This as: Daouadi, K. E., Boualleg, Y., Guehairia, O. & Taleb-Ahmed, A. (2025). Tracking the Global Pulse: The first public Twitter dataset from FIFA World Cup, Journal of Computational Social Science.

Bittensor Subnet 13 X (Twitter) Dataset

  Miner Data Compliance Agreement

In uploading this dataset, I am agreeing to the Macrocosmos Miner Data Compliance Policy.

  Dataset Summary

This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning… See the full description on the dataset page: https://huggingface.co/datasets/goldentraversy07/x_dataset_2025.

Dieser Datensatz enthält das von 𝕏 (vormals Twitter) erstellte Datenarchiv des Accounts @textarchiv des Deutschen Textarchivs (DTA). Das Archiv beinhaltet 1994 Tweets und die auf dieser Plattform getätigten Interaktionen. Erstellt wurde das Archiv am 22. Januar 2025 um 17:20:39 GMT+1. Der Account @textarchiv wurde am 19. April 2012 erstellt und am 22. Januar 2025 gelöscht.

This data set contains the data archive created by 𝕏 (formerly Twitter) for the @textarchiv account of the German Text Archive (DTA). The archive contains 1994 tweets and the interactions made on this platform. The archive was created on 22 January 2025 at 17:20:39 GMT+1. The account @textarchiv was created on 19 April 2012 and deleted on 22 January 2025.

Creative Commons Attribution 4.0 International (CC BY 4.0)

This dataset is licensed under the Creative Commons Attribution 4.0 International License.

To use this dataset, you must provide appropriate credit by citing the following paper: Hossen, M. S., Shaha, P., & Saiduzzaman, M. (2025). Social Media Sentiments Analysis on the July Revolution in Bangladesh: A Hybrid Transformer Based Machine Learning Approach. In Proceedings of the 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE.

Bibtex Entry for LaTeX users: @inproceedings{hossen2025july, author = {Hossen, Md Sabbir and Shaha, Pabon and Saiduzzaman, Md}, title = {Social Media Sentiments Analysis on the July Revolution in Bangladesh: A Hybrid Transformer Based Machine Learning Approach}, booktitle = {Proceedings of the 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)}, year = {2025}, publisher = {IEEE} }

The Student-People Mass Uprising Public Sentiments Dataset comprises 4,200 manually collected and labeled Bangla-language social media comments related to the July Revolution in Bangladesh, 2024. Sourced from Facebook, YouTube, and Twitter (X), the dataset includes an equal distribution of 1,400 positive, 1,400 negative, and 1,400 neutral comments to ensure class balance. Each comment was annotated based on clear sentiment criteria, reflecting real-world public reactions to a significant political event. This dataset is structured in CSV format and is intended for use in sentiment analysis, natural language processing, and low-resource language research. It can serve as a benchmark for developing and evaluating machine learning models for Bangla text classification and social media sentiment analysis.

Recommended Use: Academic and applied research in sentiment analysis, Bangla NLP, and social media mining.

This dataset contains information about links to resources in Trove that were shared on Twitter between 2009 and 2020.

The tweet data was compiled using Twarc in May 2021, under Twitter's academic access program. The search queries used were:

• url:nla.gov.au/nla.news
• url:trove.nla.gov.au
• url:newspapers.nla.gov.au

From the raw tweet data I extracted the Trove urls from either the entities -> urls field, or by running a regular expression over the tweet text. Where necessary, I attempted to unshorten any shortened links.

Many of the tweets were produced by bots. Using my Trove bots Twitter list, I separated the tweets into two files, one for bots and one for ordinary users.

To respect user intentions and comply with the Twitter API terms of use, I've removed all the tweet information except for tweet_id and tweet_date from the files. If it hasn't been deleted, the full data for each tweet can be obtained from the X API using the tweet_id, though this would probably require a paid subscription.

Links in tweets data

The main data files are:

• trove_url_tweets.csv – links shared by human users (although it may include some unidentified bots)
• trove_url_tweets_bots.csv – links shared by bots

Both files contain the following fields:

• tweet_id
• tweet_date
• trove_url – the shared url
• trove_type – type of Trove resource, possible values include:
  • article – an individual newspaper article
  • page – a page of a newspaper
  • title – a newspaper title
  • work – an individual Trove resource from outside the newspapers category
  • other – anything else, including search queries and links to the home page
• trove_id – the identifier of the Trove resource (extracted from the url)

In addition, the trove_url_tweets.csv file contains the following field:

• nla_official – this is set to True or False and indicates whether the tweet originated from one of the NLA's official Twitter accounts.

Some tweets contain multiple links. The datasets include one row for each link. This means that a single tweet_id can appear multiple times.

Other data files

In addition, I created a few derivative data files:

• trove_url_totals.csv
• active_users_per_year.csv
• active_bots_per_year.csv

trove_url_totals.csv

This file contains information about the number of times each link was shared by users (not including bots). The file includes the following fields:

• trove_id – Trove identifier, using this and trove_type you can query the Trove API for further information
• trove_type – type of Trove resource, possible values include:
  • article – an individual newspaper article
  • page – a page of a newspaper
  • title – a newspaper title
  • work – an individual Trove resource from outside the newspapers category
  • other – anything else, including search queries and links to the home page
• tweets – number of tweets containing a link to this resource
• retweets – number of retweets containing a link to this resource
• quotes – number of quote tweets containing a link to this resource
• total – the total number of times this link was shared (sum of tweets, retweets, quotes)

active_users_per_year.csv

This file contains information about the number of unique users each year who shared a link to Trove. The file includes the following fields:

• year
• users – number of unique users who shared a Trove link in this year

active_bots_per_year.csv

This file contains information about the number of active bots each year that shared links to Trove. The file includes the following fields:

• year
• bots – number of bots that shared a Trove link in this year

Some summary information

Number of unique users sharing Trove links	9,294
Number of bots sharing Trove links	43
Number of tweets by humans containing Trove links	48,293
Number of tweets by bots containing Trove links	318,797
Number of unique links shared by humans	36,886
Number of unique links shared by bots	270,501

See this blog post for more information.

The number of Twitter users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.9 million users (+5.1 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 18.55 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).

AI Thread Engagement Rate Predictor Dataset

This dataset contains a real-world, manually collected sample of 14 threads posted on X (formerly Twitter) under this account between September 2024 and January 2025. Despite its small size, it is an authentic dataset with real engagement metrics, making it ideal for small-scale experiments, educational purposes, and exploratory analysis of how post features influence engagement.

  📌 Purpose

The dataset is designed to help… See the full description on the dataset page: https://huggingface.co/datasets/PulkitSahu/The-AI-Thread-Engagement.

This research comprises two distinct collections of economy-related posts from the X (formerly Twitter) platform – one spanning 2007-2020 (pre-pandemic) and the other 2021-2023 (post LLM training cutoff) – alongside corresponding LLM-generated analyses of the 2021-2023 posts. These collections, curated using targeted keywords, along with the LLM analyses, are provided to facilitate investigations into the potential of economic narratives and their influence. For more information about the data collection methodology, please refer to the paper:
Almog Gueta, Amir Feder, Zorik Gekhman, Ariel Goldstein, & Roi Reichart. (2025). Can LLMs Learn Macroeconomic Narratives from Social Media? " https://doi.org/10.48550/arXiv.2406.12109">arXiv:2406.12109.

The data provided here are the post (tweet) IDs for the pre-pandemic dataset and the LLM-generated analyses for the pre-pandemic data collection. The post-LLM training cutoff data collection could not be shared due to platform data sharing restrictions.

Social Business Intelligence (BI) Market Outlook

The global Social Business Intelligence (BI) market is set to witness significant growth, with the market size expected to surpass USD 15.2 billion by 2023 and projected to reach approximately USD 35.8 billion by 2032, reflecting a robust CAGR of 9.8% throughout the forecast period. This impressive growth is primarily driven by the increasing adoption of data-driven decision-making processes across various industries, fueled by the rapid advancements in artificial intelligence and machine learning technologies. Moreover, the integration of social media analytics into business intelligence solutions is offering new avenues for organizations to glean actionable insights, thereby boosting the overall demand for social BI solutions.

One of the key growth factors propelling the Social BI market is the surge in social media usage across the globe. Businesses are leveraging data from platforms like Facebook, Twitter, and LinkedIn to gain insights into consumer behavior, preferences, and trends, which are invaluable for strategic planning. The ability of Social BI tools to analyze unstructured data from social media and transform it into structured, actionable insights is empowering businesses to enhance their customer engagement strategies, optimize marketing campaigns, and stay ahead in the competitive landscape. This increased focus on customer-centric approaches and personalized marketing is expected to significantly contribute to market growth.

Another major driver is the growing need for real-time analytics in business operations. In today's fast-paced world, organizations are increasingly reliant on the ability to make quick and informed decisions. Social BI solutions provide real-time data analytics capabilities that enable businesses to monitor and respond to social media trends as they occur. This real-time insight is crucial for mitigating risks, managing brand reputation, and maintaining a competitive edge. Furthermore, advancements in cloud computing have facilitated the deployment of social BI solutions, making them more accessible and scalable, thus further propelling market expansion.

The increasing integration of AI and machine learning technologies into Social BI solutions is also a significant growth factor. These advanced technologies enhance the capabilities of BI tools by enabling more sophisticated data analysis and predictive analytics. This integration allows businesses to anticipate market trends, automate data processing, and generate deeper insights from complex datasets. As a result, companies are increasingly investing in Social BI solutions to harness the power of AI-driven analytics for strategic decision-making, leading to a substantial increase in market demand.

Regionally, North America is expected to dominate the Social BI market due to the early adoption of advanced technologies and the presence of major industry players in the region. The Asia Pacific region, however, is projected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation, increasing social media penetration, and growing adoption of cloud-based solutions in countries like China, India, and Japan. The region's robust economic growth and expanding IT infrastructure further contribute to the market's potential in Asia Pacific.

Component Analysis

In the Social BI market, the component segment is primarily divided into software and services. The software component is expected to hold the largest market share, driven by the increasing demand for advanced analytical tools that can harness data from social media platforms. Social BI software solutions offer a wide range of functionalities, including data visualization, dashboard creation, and predictive analytics, which are essential for businesses to interpret and act upon social data effectively. The growing emphasis on digital transformation across industries has led to a surge in demand for comprehensive software solutions that facilitate seamless integration with existing business processes.

On the other hand, the services segment, which includes consulting, implementation, training, and support services, is anticipated to grow at a significant rate. As organizations increasingly adopt Social BI tools, the need for expert guidance in selecting the right solutions and optimizing their implementation becomes critical. Service providers play a vital role in ensuring that businesses can fully leverage the benefits of Social BI technologies, leading to increased demand for professional services. Furth

Birdwatch Archive

This dataset contains archive of X (formerly Twitter) Community Notes data, updated daily since June 8, 2025. Community Notes is X's collaborative fact-checking system that allows users to add context to potentially misleading posts.

  Data Files

The dataset contains TSV files updated daily:

Notes - All Community Notes with content, classifications, and metadata Ratings - User ratings and feedback on notes
Note Status History - Status changes and… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/birdwatch.

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.

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.

The number of Twitter users in Africa was forecast to continuously increase between 2024 and 2028 by in total 28.1 million users (+100.75 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 55.96 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 Australia & Oceania and North America.

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.

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, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.