This machine-generated dataset simulates social media engagement data across various metrics, including likes, shares, comments, impressions, sentiment scores, toxicity, and engagement growth. It is designed for analysis and visualization of trends, buzz frequency, public sentiment, and user behavior on digital platforms.

The dataset can be used to:

Identify spikes or drops in engagement

Analyze changes in sentiment over time

Build dashboards for digital trend tracking

Test algorithms for sentiment analysis or trend prediction

According to a global report conducted between January 2021 and December 2023, TikTok had the highest engagement rate per post when compared to Instagram, Facebook, and X (formerly Twitter). Overall, TikTok's average engagement rate per post was 2.65 percent in 2023. Instagram's engagement rate stood at 0.7 percent in 2023, and Facebook's stood at 0.15 percent.

About Dataset This dataset captures the pulse of viral social media trends across Facebook, Instagram and Twitter. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

Trend analysis 🔍 Sentiment modeling 💭 Understanding influencer marketing 📈 Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

Based on a 2023 survey across 18 countries, it was found that approximately ** percent of respondents used product reviews on social media platforms, specifically related to food, to make informed choices. Following closely, reviews related to clothing or shoes were relied upon by ** percent of consumers.

This dataset contains the number of NER (Named Entity Recognition) labels and engagement reactions (likes, shares, and comments) associated with each Facebook post in the dataset. The posts come from 439 Canadian companies listed in the international directory of manufacturing companies on the Dun & Bradstreet website.

This statistic displays a ranking of the social media platforms on which French influencers stated getting the most engagement in 2019. It appears that **** percent of the respondents declared that Instagram was the social network on which they gathered the most engagement.

Profile growth - the growth on our social platforms to see where and when we're gaining followers. Engagement rate - a ratio of how many people interacted with ours posts based on when users are usually online. Reach - the number of feeds our posts appeared in (doesn't mean people interacted with the post).

Problem Statement

👉 Download the case studies here

A global consumer goods company struggled to understand customer sentiment across various social media platforms. With millions of posts, reviews, and comments generated daily, manually tracking and analyzing public opinion was inefficient. The company needed an automated solution to monitor brand perception, address negative feedback promptly, and leverage insights for marketing strategies.

Challenge

Analyzing social media sentiment posed the following challenges:

Processing vast amounts of unstructured text data from multiple platforms like Twitter, Facebook, and Instagram.

Accurately interpreting slang, emojis, and nuanced language used by social media users.

Identifying trends and actionable insights in real-time to respond to potential crises or opportunities effectively.

Solution Provided

An advanced sentiment analysis system was developed using Natural Language Processing (NLP) and sentiment analysis algorithms. The solution was designed to:

Classify social media posts into positive, negative, and neutral sentiments.

Extract key topics and trends related to the brand and its products.

Provide real-time dashboards for monitoring customer sentiment and identifying areas of improvement.

Development Steps

Data Collection

Aggregated data from major social media platforms using APIs, focusing on brand mentions, hashtags, and product keywords.

Preprocessing

Cleaned and normalized text data, including handling slang, emojis, and misspellings, to prepare it for analysis.

Model Training

Trained NLP models for sentiment classification using supervised learning. Implemented topic modeling algorithms to identify recurring themes and discussions.

Validation

Tested the sentiment analysis models on labeled datasets to ensure high accuracy and relevance in classifying social media posts.

Deployment

Integrated the sentiment analysis system with a real-time analytics dashboard, enabling the marketing and customer support teams to track trends and respond proactively.

Monitoring & Improvement

Established a continuous feedback mechanism to refine models based on evolving language patterns and new social media trends.

Results

Gained Actionable Insights

The system provided detailed insights into customer opinions, helping the company identify strengths and areas for improvement.

Improved Brand Reputation Management

Real-time monitoring enabled swift responses to negative feedback, mitigating potential reputation risks.

Informed Marketing Strategies

Insights from sentiment analysis guided targeted marketing campaigns, resulting in higher engagement and ROI.

Enhanced Customer Relationships

Proactive engagement with customers based on sentiment analysis improved customer satisfaction and loyalty.

Scalable Monitoring Solution

The system scaled efficiently to analyze data across multiple languages and platforms, broadening the company’s reach and understanding.

Customer Engagement Data from Social Media Users

The NCAA Division I Men's Basketball Tournament, more commonly known as March Madness, is one of the highlights of the sporting calendar, drawing in millions of TV viewers each year. During a March 2023 survey in the United States, ** percent of avid sports fans who intended to watch the NCAA tournament stated that they would also engage with social media during the tournament.

This dataset provides detailed, event-level social media engagement metrics, tracking user interactions across multiple platforms and content types. It enables brands to analyze engagement trends, measure campaign effectiveness, and optimize content strategies for maximum reach and impact. With granular data on user actions, post metadata, and audience demographics, it supports advanced analytics and actionable insights for marketing teams.

The social media management market size is estimated to rise from $25.7 billion in 2024 to $270.09 billion by 2035, growing at a CAGR of 23.8% from 2024 to 2035.

The global sports social media platform market is experiencing robust growth, projected to be valued at $2517.1 million in 2025. While the exact CAGR isn't provided, considering the rapid adoption of social media and the increasing engagement with sports globally, a conservative estimate of 15% CAGR from 2025-2033 seems plausible. This would place the market size at approximately $7,857 million by 2033. Key drivers include the rising popularity of live streaming, the increasing use of mobile devices for sports consumption, and the growing engagement of sports fans through interactive features on these platforms. The younger demographics (18-24 and 25-34 years old) are significant segments, representing a large portion of active users, fueled by their preference for video and photo-sharing platforms. However, the market also shows potential for growth among older demographics (35-44 and over 45), indicating an expanding reach across age groups. The market is segmented by platform type, with video and photo sharing platforms currently dominating, but news sharing platforms are also gaining traction, particularly among those seeking real-time updates and analysis. The competitive landscape is highly dynamic, featuring established giants like Meta Platforms, Twitter, and YouTube, alongside emerging players like TikTok and niche platforms focusing on specific sports. Geographic expansion, especially in rapidly developing Asian markets like China and India, presents significant opportunities for growth. Challenges include maintaining user engagement, ensuring platform security, and managing the spread of misinformation or harmful content. The success of sports social media platforms hinges on their ability to provide engaging content, interactive features, and personalized experiences. The integration of advanced analytics, allowing for better content creation and targeted marketing, plays a significant role in the market's continued expansion. Furthermore, the increasing adoption of augmented and virtual reality technologies promises to further enhance the user experience and provide new opportunities for monetization. The evolving regulatory landscape, including concerns about data privacy and content moderation, presents a challenge that companies must navigate effectively to ensure sustainable growth. Overall, the market displays immense potential, driven by user engagement, technological advancements, and a global passion for sports.

In 2019, fashion brands performed best on photo-sharing platform Instagram. On average, fashion brands posted 0.96 posts on Instagram per day and generated a 0.68 percent engagement rate per post, which was a higher engagement rate than on Facebook or Twitter.

Promoted Social Media Posts marketing different programs and departments in Pierce County government

The global sports social media platform market is experiencing robust growth, driven by the increasing popularity of sports, the rise of mobile internet usage, and the engagement of younger demographics with social media. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This growth is fueled by several key factors: the increasing adoption of live streaming and interactive features by platforms, the expansion of sports-specific social media features (e.g., fantasy leagues integration, live score updates, and direct-to-fan athlete interactions), and the growing preference for consuming sports content digitally. The 18-24 year old demographic represents a significant portion of the market, followed closely by the 25-34 year old segment, indicating a strong potential for sustained future growth as these demographics age into higher disposable income brackets. The video and photo sharing segment currently dominates, while news sharing platforms are gaining traction, representing an area of significant opportunity. Geographical expansion into rapidly developing economies in Asia-Pacific and Africa, alongside strategic partnerships with professional sports leagues and athletes, further contribute to the market’s expansion. However, challenges remain. Competition among established players like Meta Platforms, Twitter, and TikTok, coupled with the emergence of niche sports-focused platforms, create a fiercely competitive landscape. Data privacy concerns and the need for effective content moderation to combat misinformation and harmful content pose significant restraints. Maintaining user engagement and monetization strategies in a constantly evolving digital environment are also crucial for platform success. The market is segmented by age group (18-24, 25-34, 35-44, and over 45) and platform type (video and photo sharing, and news sharing). North America currently holds a significant market share, but the Asia-Pacific region is demonstrating the most rapid growth, reflecting its large and increasingly digitally engaged population.

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Social Media Analytics Market Size 2025-2029

The social media analytics market size is forecast to increase by USD 21.2 billion, at a CAGR of 35.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing availability and complexity of data. Companies are capitalizing on the wealth of information generated through social media platforms to gain valuable insights into consumer behavior and market trends. Furthermore, the surging use of advanced targeting options enables businesses to reach their audiences more effectively, enhancing their marketing efforts. However, interpreting unstructured data from social media sources poses a challenge. The vast amount of data, combined with its unstructured nature, necessitates sophisticated analytics tools and expertise to extract meaningful insights. As businesses navigate this complex landscape, they must invest in technologies and strategies that can effectively harness the power of social media analytics to stay competitive and make informed decisions.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the constant innovation and adaptation of social media networks and the increasing importance of digital communication in business. Social media scheduling and automation streamline content distribution across various channels, enabling brands to maintain a consistent presence. Reputation management and monitoring tools provide real-time insights into public perception, allowing for swift response to customer feedback and crisis management. Social listening and sentiment analysis offer valuable data on consumer preferences and emotions, informing marketing strategies and product development. Influencer marketing and social media advertising platforms enable targeted campaigns, reaching specific demographics and interests. Social media CRM integrates customer interactions across channels, providing a holistic view of customer engagement. Social media trends, such as the rise of ephemeral content and the increasing use of video, necessitate ongoing adaptation and optimization. Social media intelligence and engagement metrics offer insights into the effectiveness of campaigns and the impact on brand image. Social media APIs facilitate seamless data integration and analysis, enabling businesses to make data-driven decisions and stay competitive in the ever-changing social media landscape.

How is this Social Media Analytics Industry segmented?

The social media analytics 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. End-userRetailGovernmentMedia and entertainmentTravelOthersApplicationSales and marketing managementCustomer experience managementCompetitive intelligenceRisk managementPublic safety and law enforcementDeploymentOn-premisesCloudTypePredictive analyticsPrescriptive analyticsDescriptive analyticsDiagnostics analyticsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW).

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.Social media analytics plays a pivotal role in retail businesses, enabling them to analyze sales and customer engagement across various stages. Retailers utilize social media dashboards and monitoring tools for formulating effective marketing strategies and enhancing brand awareness. Social listening and influencer marketing are integral parts of this process, providing valuable insights into consumer behavior and preferences. Social media networks, such as Facebook, Twitter, and Google+, are essential channels for retailers, with over 90% of them active in 2023. These platforms offer access to vast amounts of data, which can be harnessed through social media reporting and research tools. Social media automation and scheduling facilitate consistent engagement with the audience, while sentiment analysis ensures reputation management. Social media algorithms and brand monitoring help retailers stay updated on trends and customer sentiment, enabling them to respond promptly to crises or opportunities. Social media advertising and API integrations offer targeted marketing and data access, respectively. Social media metrics and ROI are crucial indicators of success, with CRM systems providing a more comprehensive understanding of customer interactions. Retailers leverage social media insights to optimize their content, improve customer engagement, and ultimately drive sales. The market for social media analytics contin

The report examines the ways online Canadian adults are engaging politically on social media. This is the third and final report based on a census-balanced survey of 1,500 Canadians using quota sampling by age, gender, and geographical region. The other two reports in this series are: "The State of Social Media in Canada 2017" and "Social Media Privacy in Canada". The series is published by the Social Media Lab, an interdisciplinary research lab at Ted Rogers School of Management, Ryerson University. The lab studies how social media is changing the ways in which people communicate, share information, conduct business and how these changes are impacting our society.

This dataset captures a vibrant tapestry of emotions, trends, and interactions across various social media platforms. It provides a snapshot of user-generated content, encompassing text, timestamps, hashtags, countries, likes, and retweets. Each entry unveils unique stories—moments of surprise, excitement, admiration, thrill, and contentment—shared by individuals worldwide. It is designed to offer insights into social media dynamics and user sentiments.

Columns

• Unnamed: 0: An index column, typically for record identification within the dataset.
• Text: User-generated content, presenting the original social media post from which sentiments are derived.
• Sentiment: The categorised emotion or sentiment expressed in the 'Text' column, such as Positive, Negative, or Neutral.
• Timestamp: The exact date and time when the social media content was posted.
• User: A unique identifier for the individual user who contributed the content.
• Platform: The specific social media platform where the content originated (e.g., Twitter, Instagram, Facebook).
• Hashtags: Keywords or phrases prefixed with '#' that identify trending topics and themes within the content.
• Retweets: A numerical value reflecting the popularity of the content, indicating how many times it was shared or re-posted.
• Likes: A numerical value quantifying user engagement, representing the number of 'likes' the post received.
• Country: The geographical origin of the social media post.
• Year: The year the social media post was published.
• Month: The month the social media post was published.
• Day: The day of the month the social media post was published.
• Hour: The hour of the day the social media post was published.

Distribution

The dataset is typically provided as a data file, most often in CSV format. A sample file will be updated separately to the platform. The structure is tabular, organised into the columns described above. Specific numbers for rows or records are not available, but it represents a daily snapshot of social media activity.

Usage

This dataset is a rich source of information that can be leveraged for various analytical purposes: * Sentiment Analysis: Explore the emotional landscape by conducting sentiment analysis on the 'Text' column. Classify user-generated content into categories such as surprise, excitement, admiration, thrill, and contentment. * Temporal Analysis: Investigate trends over time using the 'Timestamp' column. Identify patterns, fluctuations, or recurring themes in social media content. * User Behaviour Insights: Analyse user engagement through the 'Likes' and 'Retweets' columns to discover popular content and user preferences. * Platform-Specific Analysis: Examine variations in content across different social media platforms using the 'Platform' column. Understand how sentiments vary across platforms. * Hashtag Trends: Identify trending topics and themes by analysing the 'Hashtags' column. Uncover popular or recurring hashtags. * Geographical Analysis: Explore content distribution based on the 'Country' column. Understand regional variations in sentiment and topic preferences. * User Identification: Utilise the 'User' column to track specific users and their contributions. Analyse the impact of influential users on sentiment trends. * Cross-Analysis: Combine multiple features for in-depth insights. For example, analyse sentiment trends over time or across different platforms and countries.

Coverage

The dataset offers global geographical coverage, capturing posts from individuals worldwide. Examples in the sample data include content originating from the USA, Canada, the UK, Australia, and India. The data represents a snapshot of user-generated content, with the provided sample covering a few days in January 2023. The demographic scope is tied to general social media users, with no specific demographic breakdowns noted.

License

CCO

Who Can Use It

This dataset is ideal for: * Data scientists and machine learning engineers looking to train and validate models for sentiment analysis, natural language processing, and social media analytics. * Researchers and academics studying social trends, public opinion, digital communication, and user engagement patterns. * Marketing and brand analysts seeking to understand consumer sentiment, track brand mentions, and evaluate the reception of campaigns across different social platforms. * Anyone interested in gaining insights into the emotional landscape and dynamic interactions occurring within social media environments.

Dataset Name Suggestions

• Social Media Sentiments Analysis Dataset
• Global Social Sentiment Tracker
• User-Generated Content Emotion Data
• Digital Conversation Sentiment Snapshot
• Social Media Engagement Sentiment

Attributes

Original

The data stems from a survey study (online questionnaire) of HE students in Switzerland, conducted in 2019.