How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

This dataset provides a comprehensive analysis of the factors influencing the credibility of information on social media among Iranian users. The research focuses on identifying the most significant factors that affect the perceived credibility of information shared on various social media platforms. The dataset includes demographic information, social media usage patterns, and ratings of various attributes related to information credibility.

Key Features

• Demographic Data: Includes age, gender, education level, study field, and university.
• Social Media Usage: Details on the number of social media memberships, active platforms, and average hours spent per day.
• Credibility Factors: Ratings on various attributes such as source trustworthiness, media structure, message accuracy, and more.
• Comprehensive Coverage: Covers multiple dimensions of social media usage and information credibility.
• User-Centric Insights: Provides insights into how users perceive and interact with information on social media.

Potential Uses

• Academic Research: Investigate the factors that influence information credibility on social media.
• Social Media Analysis: Understand user behavior and preferences on social media platforms.
• Policy Development: Inform policies related to information dissemination and credibility on social media.
• Marketing and Advertising: Tailor content strategies based on user perceptions of credibility.
• User Experience Design: Improve the design and functionality of social media platforms to enhance information credibility.

This dataset contains simulated data for social media users' demographics, behaviors, and perceptions related to political content. It includes features such as age, gender, education level, occupation, social media usage frequency, exposure to political content, and perceptions of accuracy and relevance.

the features included in the "Social Media Political Content Analysis Dataset":

1. Age: Age of the user.
2. Gender: Gender identity of the user.
3. Education Level: Highest level of education attained by the user.
4. Occupation: Current occupation of the user.
5. Political Affiliation: Political leaning or affiliation of the user (e.g., Liberal, Conservative, Independent).
6. Geographic Location: Country or region where the user is located (e.g., USA, UK, Canada, Australia).
7. Social Media Usage Frequency: Frequency of social media usage by the user (e.g., 0-1 hour, 1-2 hours, 2-4 hours, 4+ hours).
8. Preferred Social Media: Social media platform preferred by the user (e.g., Facebook, Twitter, Instagram).
9. Political Content Exposure: Frequency of exposure to political content on social media (e.g., Once a day, Few times a week, Rarely, Several times a day).
10. Types of Political Content: Types of political content consumed by the user (e.g., News articles, Opinion pieces, Memes).
11. Sources of Political Content: Sources from which the user obtains political content (e.g., Mainstream media, Political parties, Independent bloggers).
12. Recency of Exposure: Recency of the user's exposure to political content (e.g., Within the last hour, Within the last 24 hours, Within the last week, Longer than a week ago).
13. Interactions Frequency: Frequency of user interactions with political content on social media (e.g., Once a day, Few times a week, Rarely, Several times a day).
14. Political Content Topics: Topics of political content that interest the user (e.g., Economy, Healthcare, Immigration, Environment).
15. Perception of Accuracy: User's perception of the accuracy of political content on social media (e.g., Very accurate, Somewhat accurate, Not accurate).
16. Awareness of Algorithms: Whether the user is aware of algorithms that determine their social media feed (e.g., Yes, No).
17. Perception of Relevance: User's perception of the relevance of political content on social media (e.g., Very relevant, Somewhat relevant, Not relevant).
18. Personal Impact: User's perception of the personal impact of political content on social media (e.g., Strong impact, Moderate impact, No impact).
19. Trust in Social Media: User's level of trust in social media as a source of political information (e.g., Trust a lot, Trust somewhat, Do not trust).
20. Concerns about Algorithms: User's level of concern about algorithms shaping their social media experience (e.g., Very concerned, Somewhat concerned, Not concerned).
21. Overall Quality of Discourse: User's perception of the overall quality of political discourse on social media (e.g., High quality, Moderate quality, Low quality).
22. Views on Influence: User's perception of the influence of political content on social media (e.g., Very influential, Somewhat influential, Not influential).
23. Suggestions for Improvement: User's suggestions for improving the quality or experience of political content on social media (e.g., Increase transparency, Provide more diverse sources, Improve fact-checking, Enhance user controls).

Over the five years through 2025-26, industry revenue is forecast to expand at a compound annual rate of 20.3% to reach £12.5 billion. Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the past five years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform has over 30 million monthly users in the UK in 2025. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. In 2025, more social media platforms are using AI to boost user engagement. This improves click-through rates and drives higher advertising revenue. Industry revenue is expected to grow by 6.3% in 2025-26. Over the five years through 2030-31, social media platforms' revenue is projected to climb at an estimated 9.2% to reach £19.4 billion. Regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The tightening of regulations will raise industry compliance costs, weighing on profit margin. Older age groups present a new revenue opportunity for social media platforms if they can bridge the gap between passive TV consumption and interactive digital engagement. Augmented Reality (AR) technology will move beyond filters to become standard for immersive product trials, interactive ads, and virtual meetups

Social Media Disaster-Related Discussions

Detecting Relevant Content with Trusted Judgments

By CrowdFlower [source]

About this dataset

Welcome to the disaster tweets dataset! This collection of tweets holds a wealth of information about global disasters and their effects on people, governments, and organizations all over the world. With over 10,000 tweets collected and carefully annotated with labels of whether they reported an actual disaster or not, this dataset provides unique insight into what these events look like in terms of social media conversations.

This information is derived from a variety of key terms related to disaster events, such as “ablaze” and “pandemonium” which was used to gather each individual tweet for analysis. The columns for each tweet include detailed metadata about the user who posted it along with variables such as keyword relevance and location. Alongside all these attributes is the core text belonging to each individual tweet- giving you access to all sorts of stories from natural disasters, contagious disease outbreaks or conflicts between nations that can be found in one place!

So whatever you're looking for - whether it's observations about first-hand accounts or conducting research on public sentiment during a major event - this dataset offers you an invaluable source full of timely information that could potentially save lives down the line. So take your journey through this data now and embark upon discovering what devastation looks like through social media!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains tweets related to disaster events, including the keyword, location, text, tweetid and userid. It provides insights into how people interact with each other on social media during a disaster. Using this dataset you can gain valuable insight into the dynamics of online communication in disasters and provide an important point of reference for future disaster management initiatives.

Research Ideas

• Analyzing the effectiveness of disaster relief and humanitarian aid efforts, by mapping tweets against public data of areas affected by disasters and donations made to help those affected.
• Developing advanced statistical models to predict the magnitude and impact of an oncoming natural disaster using keyword analysis in social media posts related to past disasters.
• Creating text-based classifiers to accurately detect disaster-related tweets in real-time, allowing emergency services providers early warning signs before a potential event occurs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: socialmedia-disaster-tweets-DFE.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------------------------| | _golden | A boolean value indicating whether the tweet is a golden tweet or not. (Boolean) | | _unit_state | The state of the tweet (e.g. finalized, judged, etc.). (String) | | _trusted_judgments | The number of trusted judgments for the tweet. (Integer) | | _last_judgment_at | The date and time of the last judgment for the tweet. (DateTime) | | choose_one | The label assigned to the tweet (e.g. relevant, not relevant, etc.). (String) | | choose_one_gold | The gold label assigned to the tweet (e.g. relevant, not relevant, etc.). (String) | | keyword | The keyword associated with the tweet. (String) | | location | The location associated with the tweet. (String) | | text | The text content of the tweet. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit CrowdFlower.

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

https://media.giphy.com/media/3GSoFVODOkiPBFArlu/giphy.gif" alt="social">

The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

https://media.giphy.com/media/2tSodgDfwCjIMCBY8h/giphy.gif" alt="socialcat">

Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

SMDRM - Social Media for Disaster Risk Management

Social media has been described as a form of distributed cognition, a mechanism for understanding a situation using information spread across many minds. The interactions among people in social media are a form of collective intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modelling or monitoring systems can assimilate during a crisis. Gaining situational awareness in a disaster is critical and time-sensitive. Social media presents the possibilities of a growing data source to help improve response in the early hours and days of a crisis. However, social media platforms may not provide the functionality of summarising the information that is useful for crisis responders.SMDRM is a software platform that streamlines the processing of text and images extracted from Twitter in near real-time during a specific event. The data is collected using a combination of keywords and locations based on daily forecasts from the early warnings systems of the Copernicus Emergency Management Service such as EFAS, GloFAS and EFFIS (emergency.copernicus.eu) or triggered manually in case of earthquakes or not-forecasted events. Text is automatically "annotated" using a binary multilingual classifier trained on 12 languages and extended with multilingual embeddings. Simultaneously, a multi-class convolutional neural network labels relevant images for floods, storms, earthquakes and fires. The information that doesn't embed coordinates is geolocated in a two-step algorithm where location candidates are first selected using a multilingual named-entity recognition tool and then searched on available gazetteers. The last step of the SMDRM data processing is the aggregation of relevant information in spatial (administrative areas) and temporal (daily) units. Social media activity about an event can finally be distributed as a data map and visualised on a map server and made available to users.SMDRM could offer timely information useful for reducing the hazard models' uncertainty and providing added-value information such as reports or descriptions of the situation on the ground or in the vicinity. Other stakeholders, such as research groups could access new data to complement the ones extracted from traditional sensors or earth observation. The platform can adapt to cope with the varying workload as it uses scalable software containers. If the number of tweets is higher during an impactful event, the platform can use more containers to annotate them. SMDR code, together with the tens of thousands of annotated social media messages used for training its models, will be released as an open-source platform whose modules can be adapted to serve other research projects. We describe the platform's architecture and implementation details, and two use cases where images and text were used as a use-case to test the system's modules.

Source https://ui.adsabs.harvard.edu/abs/2021EGUGA..2315012L/abstract

Social Media Engagement (2025)

This dataset contains 20,000 synthetic social media posts crafted to mimic realistic user activity on a fictional platform. It simulates various user demographics, post content, hashtags, topics, and detailed engagement metrics such as likes, comments, and shares.

Overview

Each record represents a unique social media post made by a user, enriched with features that allow for analysis of trends, behavior, and engagement. The dataset includes:

• User-level information: age, gender, followers, verified status, etc.
• Post-level information: topic, hashtags, media, engagement
• Platform and device data
• Calculated engagement rate

Column Descriptions

**Description

To ensure the relevance and recency of the collected data, we further narrowed our dataset by limiting the publication years to the period spanning from 2010 to 2023. This restriction aimed to capture the most contemporary and pertinent research in social media surveillance.

After this limitation, the dataset consisted of 927 academic publications. This meticulous search and selection process laid the foundation for our systematic and bibliometric analysis, allowing us to comprehensively examine the scholarly discourse surrounding social media surveillance, identify key themes, and assess the intellectual structure of the field from 2010 to 2023.

The dataset comprises 927 academic publications spanning from 2010 to 2023, focusing on social media surveillance. Below is a description of the dataset columns:

Authors: Names of the authors who contributed to the publication. Author full names: Full names of the authors with their respective author IDs. Author(s) ID: Unique identifiers assigned to each author. Title: Title of the publication. Year: Year of publication. Source title: Name of the journal, book, or conference where the publication appeared. Volume: Volume number of the source. Issue: Issue number of the source. Art. No.: Article number within the source. Page start: Starting page number of the publication. Page end: Ending page number of the publication. Page count: Total number of pages of the publication. Cited by: Number of times the publication has been cited by other works. DOI: Digital Object Identifier for the publication. Link: URL link to access the publication. Affiliations: Institutions or organizations associated with the authors. Authors with affiliations: Authors listed with their respective affiliations. Abstract: Summary of the publication. Author Keywords: Keywords provided by the authors. Index Keywords: Keywords assigned by indexing services. Molecular Sequence Numbers: Identifiers for molecular sequences mentioned. Chemicals/CAS: Chemical Abstracts Service numbers for chemicals mentioned. Tradenames: Trade names of products or chemicals mentioned. Manufacturers: Manufacturers of products or chemicals mentioned. Funding Details: Information about the funding sources. Funding Texts: Detailed text describing the funding sources. References: List of references cited in the publication. Correspondence Address: Contact address for correspondence with authors. Editors: Names of the editors of the publication. Publisher: Name of the publisher. Sponsors: Sponsors of the publication or research. Conference name: Name of the conference where the paper was presented. Conference date: Date of the conference. Conference location: Location of the conference. Conference code: Code associated with the conference. ISSN: International Standard Serial Number for the source. ISBN: International Standard Book Number for the source. CODEN: Six-character code used to identify periodicals. PubMed ID: Identifier for publications indexed in PubMed. Language of Original Document: Language in which the publication was written. Abbreviated Source Title: Abbreviated title of the source. Document Type: Type of document (e.g., article, book chapter). Publication Stage: Stage of publication (e.g., final, in press). Open Access: Open access status of the publication. Source: Database source of the publication (e.g., Scopus). EID: Electronic Identifier for the publication.

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis. Social media: trust and consumption Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media. What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis. Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers. Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Citation-worthy statistics and data points related to Social Media Management Services. This dataset provides key metrics and statistical information.

This dataset contains metadata about all Covid-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. We publish the data alongside the code used to produce on Github. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.

Target group study with intensive users of social networks. The focus was on the frequency of use, significance and purpose of use of social media. Further questions were: How do social networks affect the information behaviour of their users? Where do social networks come into contact with political content or topics? Other focal points were political information behaviour and the credibility of social media. In addition, the following research questions were examined: Which information offerings of the Federal Government are important and which are actually used? What are the expectations of these information services? What are the respondents´ attitudes towards the relationship between politics and social networks?

Topics: Social media use: frequency of use of social media; platforms used at least once a day; ranking of the four most frequently used social networks; preferred devices for social media use; type of use or reasons for use; attitude towards social media in general with regard to time spent, credibility of information, quality of social contacts, relevant information and life without social networks.

Politics and media: political interest; points of contact with politics in everyday life; frequency of use of various media offers for political information; attitude towards politics and political information in social networks with regard to trustworthiness, credibility and orientation.

Political information behaviour in social networks: frequency of interaction with political activities of others in social networks; frequency of own political activities in social networks; seen political content on the social networks Facebook, Instagram, Twitter, Youtube and WhatsApp; opinion on political posts and contributions in the used social networks; personal consequences of political oversupply; Facebook users: active search for political content or automatic display in newsfeed; comparison of the quality of political discussions within and outside social networks; friends in social networks personally known; similar or rather different political views of these friends; own reaction to political posts of friends; following politicians, parties and political institutions in social networks; following persons and organizations sharing personal political opinions or with different political opinions; reasons why the respondent follows politicians etc. in social networks; perception of hate comments; evaluation of the law against hate comments.

Information behaviour concerning the Federal Government: importance of selected information offerings of the Federal Government; perception and frequency of use of these information offerings; expectations of information offerings of the Federal Government in social networks.

Living conditions: assessment of one´s own economic situation; satisfaction with democracy; democracy as a good form of government; party identification.

Demography: sex; age; household size; education; gainful employment; occupational status; federal state; city size; net household income.

Additionally coded was: weighting factor.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Social media algorithms now shape the information we see. In 2025, over 5.4 billion people will engage with personalized feeds daily, each tailored by complex models that sort, amplify, or bury content based on real-time behavior. These algorithms affect everything from advertising campaigns to political messaging, and even how quickly...

Snapshot img

Social Media Listening Market Size 2025-2029

The social media listening market size is forecast to increase by USD 4.87 billion at a CAGR of 8.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing usage of social media platforms worldwide. With over 4.3 billion users as of 2021, social media has become a powerful tool for businesses to engage with their customers and gain valuable insights into consumer behavior and preferences. A key trend in this market is the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in social media listening solutions, enabling more accurate and efficient data analysis. However, this market is not without challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect user data.
Companies must ensure they have strong data security measures in place to comply with these regulations and maintain consumer trust. Additionally, the vast amount of data generated on social media requires sophisticated analytics tools to extract meaningful insights. As such, businesses seeking to capitalize on the opportunities presented by the market must invest in advanced analytics solutions and prioritize data security and privacy. By doing so, they can effectively navigate the challenges and stay ahead of the competition.

What will be the Size of the Social Media Listening Market during the forecast period?

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Social media listening has emerged as a crucial business tool, enabling organizations to gain valuable insights from the vast amount of data generated through social media activity. This data is analyzed using techniques such as topic modeling and sentiment scoring to understand consumer behavior, preferences, and trends. Social media geographics and demographics provide essential context, while social media reach and volume measure the scope and impact of conversations. Social media pulse and sentiment reflect the current sentiment and buzz surrounding specific topics, offering real-time insights into market dynamics and trends.
Social media listening software is a vital component of the global market for social media analytics. Social media influence is assessed through the size and engagement of an audience, providing valuable information for marketing and brand management strategies. The social media landscape and heatmap offer a comprehensive view of the social media ecosystem, helping businesses stay informed and adapt to evolving patterns.

How is this Social Media Listening Industry segmented?

The social media listening industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Software
  Services


End-user

  Retail and e-commerce
  IT and telecom
  BFSI
  Media and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The software segment is estimated to witness significant growth during the forecast period. This segment encompasses platforms and tools that offer real-time, automated, and scalable capabilities to monitor and analyze social media conversations across various channels such as Twitter, Facebook, Instagram, LinkedIn, TikTok, and Reddit. Real-time monitoring is a key feature of these solutions, empowering brands to identify mentions, trends, and sentiment as they emerge. By staying abreast of evolving topics, businesses can respond promptly to customer concerns, capitalize on viral events, and maintain a strong online presence. Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to social media listening software, enabling advanced topic identification, sentiment analysis, and trend recognition.

These technologies enable businesses to gain valuable customer insights, inform product development, and enhance customer experience. Social media listening platforms also offer data visualization and reporting features, allowing businesses to analyze and present their findings in a clear and actionable manner. Additionally, they provide social media dashboards, alerts, and governance tools to ensure compliance with social media policies and ethical standards. In summary, social media listening software plays a pivotal role in the global market for social media analytics, offering real-time insights and advanced capabilities to help businesses navigate the complex social media landscape and engage effectively with their audience.

Get a glance at the market report of share of v

Abstract:

This webgraph is a page-page graph of verified Facebook sites. Nodes represent official Facebook pages while the links are mutual likes between sites.

Data Set Information:

Node features are extracted from the site descriptions that the page owners created to summarize the purpose of the site. This graph was collected through the Facebook Graph API in November 2017 and restricted to pages from 4 categories which are defined by Facebook. These categories are: politicians, governmental organizations, television shows and companies. The task related to this dataset is multi-class node classification for the 4 site categories. Provide all relevant information about your data set.

Attribute Information:

Features are words that appear in the page descriptions.

Relevant Papers:

B. Rozemberczki, C. Allen and R. Sarkar. Multi-scale Attributed Node Embedding. 2019.

Citation Request:

@misc{rozemberczki2019multiscale, title={Multi-scale Attributed Node Embedding}, author={Benedek Rozemberczki and Carl Allen and Rik Sarkar}, year={2019}, eprint={1909.13021}, archivePrefix={arXiv}, primaryClass={cs.LG} }

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)