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.

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.

The social media analytics market is experiencing robust growth, driven by the escalating volume of social media data and the increasing need for businesses to understand and leverage this information for strategic decision-making. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the rising adoption of social listening tools for brand monitoring and customer feedback analysis, the growing sophistication of AI-powered analytics platforms offering predictive capabilities, and the increasing demand for real-time insights to inform marketing strategies and crisis management. Trends such as the integration of social media analytics with other data sources (e.g., CRM, web analytics) and the emergence of specialized solutions for specific industries (e.g., healthcare, finance) are further fueling market expansion. However, challenges such as data privacy concerns, the complexity of social media data, and the need for skilled professionals to interpret and utilize the insights effectively pose some restraints on market growth. The market is segmented by solution type (e.g., social listening, sentiment analysis, brand monitoring), deployment mode (cloud, on-premise), and industry vertical. Leading players like IBM, Oracle, Salesforce, and Adobe Systems are shaping the market landscape through continuous innovation and strategic acquisitions. The competitive landscape is characterized by a mix of established technology vendors and specialized social media analytics firms. While large players offer comprehensive platforms integrating social media analytics with other enterprise solutions, smaller specialized vendors focus on niche functionalities and specific industry needs. The North American region currently holds the largest market share, followed by Europe and Asia-Pacific. However, growth in emerging markets is expected to accelerate in the coming years, driven by increasing internet penetration and mobile adoption. The forecast period (2025-2033) is expected to witness significant technological advancements, potentially including the wider adoption of blockchain technology for secure data management and improved analytical capabilities based on advancements in natural language processing (NLP) and machine learning (ML).

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 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 three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two 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.

This data contain the minimal annotation for datasets described in the publication:Identifying witness accounts from social media using imagery, (2017). ISPRS International Journal of Geo-Information.

The Social Media Analytics market is experiencing robust growth, projected to reach $6.00 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24% from 2019 to 2033. This expansion is fueled by several key drivers. The increasing reliance of businesses on social media for marketing, customer service, and brand building necessitates sophisticated analytics to understand audience engagement, sentiment, and campaign effectiveness. Further driving market growth is the proliferation of social media platforms themselves, generating ever-larger datasets demanding advanced analytical tools. The rise of artificial intelligence (AI) and machine learning (ML) within these tools enhances the speed and accuracy of insights, allowing businesses to react swiftly to emerging trends and opportunities. Competitive pressures also force companies to adopt these technologies to gain a decisive advantage in market understanding and responsiveness. While data privacy regulations present a restraint, the demand for actionable insights continues to outweigh these concerns, with sophisticated analytics tools incorporating privacy-preserving techniques. Segmentation within the market includes solutions catering to different business sizes and needs, from small and medium-sized enterprises (SMEs) to large multinational corporations. Major players like Sprinklr, Synthesio, BrandWatch, Oracle, NetBase Solutions, Meltwater, Talkwalker, Sprout Social, Digimind Social, and Brand24 are shaping the competitive landscape through continuous innovation and strategic acquisitions. The forecast period of 2025-2033 promises even more dynamic growth. As social media usage continues its upward trajectory and businesses become more sophisticated in their application of analytics, we can anticipate increased demand for more advanced features such as predictive analytics, real-time monitoring, and sentiment analysis across multiple languages. The integration of social media analytics with other data sources, such as CRM and website analytics, will further enhance its value, enabling a holistic view of customer behavior and business performance. We expect to see continued investment in research and development, leading to improvements in data visualization, reporting capabilities, and the development of niche solutions tailored to specific industries. The growing adoption of cloud-based solutions will also facilitate accessibility and scalability for businesses of all sizes. Recent developments include: In May 2022, TikTok expanded its Marketing Partners Program, introducing its inaugural group of Content Marketing Partners. The founding member is Brandwatch, and its social suite of the future, which would allow its customers to scale, manage, execute, and optimize the content on TikTok, all while staying within the Brandwatch platform. This officially badged partnership between Brandwatch and TikTok empowers Brandwatch clients to manage, understand, and respond to their community profiles on TikTok in a way that feels native to the world's hottest technology platform., In April 2022, Digimind collaborated with Facelift to give essential tools for effective social media growth. Facelift is a firm that provides social media management tools. This collaboration benefited both industries in monitoring their brand image and effectively managing all social networks.. Key drivers for this market are: Exponential Growth of Number of Social Media Users, Increased Emphasis on Target Marketing and Competitive Intelligence. Potential restraints include: Exponential Growth of Number of Social Media Users, Increased Emphasis on Target Marketing and Competitive Intelligence. Notable trends are: Increased Emphasis on Targeted Marketing and Competitive Intelligence.

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Snapshot img

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 expanding availability and complexity of social media data. Businesses increasingly recognize the value of social media insights to inform marketing strategies, enhance customer engagement, and gauge brand reputation. In response, social media platforms continue to roll out advanced targeting options, enabling more precise audience segmentation and personalized messaging. However, the surging use of social media data also presents challenges. Interpreting unstructured data from various sources remains a formidable task, requiring sophisticated analytics tools and expertise.
Companies must navigate these complexities to effectively harness the power of social media analytics and stay competitive in today's digital landscape. To succeed, organizations need to invest in advanced analytics solutions, cultivate data literacy skills, and establish clear data governance policies. By addressing these challenges, businesses can unlock valuable insights from social media data and capitalize on emerging opportunities in this dynamic market.

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 Sample

The market continues to evolve, offering valuable insights for businesses across various sectors. Hashtag tracking and sentiment classification help organizations understand public perception and engagement with their brand. Engagement metrics, share of voice, and trend analysis algorithms provide valuable data for brand reputation management and customer journey mapping. Social media ROI, influencer marketing metrics, and sentiment scoring offer insights into the effectiveness of advertising campaigns. User behavior patterns, predictive modeling, and anomaly detection enable businesses to anticipate trends and respond to crises in real-time. Social media listening, lead generation attribution, influencer identification, and customer satisfaction scores provide actionable insights for community management and crisis communication management.

Data visualization dashboards and social listening tools facilitate effective audience segmentation and conversational AI. Reach forecasting, content performance, keyword analysis, and campaign effectiveness metrics offer valuable insights for optimizing social media strategies. Platform-specific insights enable businesses to tailor their approach to each social media channel. According to recent market research, the market is expected to grow by over 15% annually, reflecting the increasing importance of social media data for businesses. For instance, a retail company used social media listening tools to monitor customer conversations and identified a trend in customer complaints about product packaging. The company responded by redesigning the packaging, resulting in a 12% increase in sales.

This example highlights the potential impact of social media analytics on business performance.

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-user

  Retail
  Government
  Media and entertainment
  Travel
  Others


Application

  Sales and marketing management
  Customer experience management
  Competitive intelligence
  Risk management
  Public safety and law enforcement


Deployment

  On-premises
  Cloud


Type

  Predictive analytics
  Prescriptive analytics
  Descriptive analytics
  Diagnostics analytics


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest 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 marketing, enabling businesses to track and analyze customer engagement, sentiment, and trends in real-time. Tools such as hashtag tracking, sentiment classification, and engagement metrics help retailers understand their audience's preferences and behavior patterns. Share of voice and trend analysis algorithms provide insights into market dynamics and brand reputation management. Customer journey mapping and social media ROI measurement allow businesses to optimize their marketing strategies and improve sales. Influencer marketing metrics, sentiment scoring, and advertising

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.)

Context

Social Media has been taking up everything on the Internet. People getting the latest news, useful resources, life partner and what not. In a world where Social media plays a big role in giving news, we must also know that news which affects our sentiments are going to get spread like a wildfire. Based on the Headline and the title, and according to the date given and the Social media platforms, you have to predict how it has affected the human sentiment scores. You have to predict the column “SentimentTitle” and “SentimentHeadline”.

Content

This is a subset of the dataset of the same name available in the UCI Machine Learning Repository The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine.

Dataset Information

The attributes for each of the dataset are : - IDLink (numeric): Unique identifier of news items - Title (string): Title of the news item according to the official media sources - Headline (string): Headline of the news item according to the official media sources - Source (string): Original news outlet that published the news item - Topic (string): Query topic used to obtain the items in the official media sources - Publish-Date (timestamp): Date and time of the news items' publication - Facebook (numeric): Final value of the news items' popularity according to the social media source Facebook - Google-Plus (numeric): Final value of the news items' popularity according to the social media source Google+ - LinkedIn (numeric): Final value of the news items' popularity according to the social media source LinkedIn - SentimentTitle: Sentiment score of the title, Higher the score, better is the impact or +ve sentiment and vice-versa. (Target Variable 1) - SentimentHeadline: Sentiment score of the text in the news items' headline. Higher the score, better is the impact or +ve sentiment. (Target Variable 2)

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

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

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion, at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increased availability and diversity of data sources. This expanding data landscape is fueling the rise of alternative data-driven investment strategies across various industries. However, the market faces challenges related to data quality and standardization. As companies increasingly rely on alternative data to inform business decisions, ensuring data accuracy and consistency becomes paramount. Addressing these challenges requires robust data management systems and collaboration between data providers and consumers to establish industry-wide standards. Companies that effectively navigate these dynamics can capitalize on the wealth of opportunities presented by alternative data, driving innovation and competitive advantage.

What will be the Size of the Alternative Data 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, with new applications and technologies shaping its dynamics. Predictive analytics and deep learning are increasingly being integrated into business intelligence systems, enabling more accurate risk management and sales forecasting. Data aggregation from various sources, including social media and web scraping, enriches datasets for more comprehensive quantitative analysis. Data governance and metadata management are crucial for maintaining data accuracy and ensuring data security. Real-time analytics and cloud computing facilitate decision support systems, while data lineage and data timeliness are essential for effective portfolio management. Unstructured data, such as sentiment analysis and natural language processing, provide valuable insights for various sectors. Machine learning algorithms and execution algorithms are revolutionizing trading strategies, from proprietary trading to high-frequency trading. Data cleansing and data validation are essential for maintaining data quality and relevance. Standard deviation and regression analysis are essential tools for financial modeling and risk management. Data enrichment and data warehousing are crucial for data consistency and completeness, allowing for more effective customer segmentation and sales forecasting. Data security and fraud detection are ongoing concerns, with advancements in technology continually addressing new threats. The market's continuous dynamism is reflected in its integration of various technologies and applications. From data mining and data visualization to supply chain optimization and pricing optimization, the market's evolution is driven by the ongoing unfolding of market activities and evolving patterns.

How is this Alternative Data Industry segmented?

The alternative data 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. TypeCredit and debit card transactionsSocial mediaMobile application usageWeb scrapped dataOthersEnd-userBFSIIT and telecommunicationRetailOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.Alternative data derived from card and debit card transactions plays a pivotal role in business intelligence, offering valuable insights into consumer spending behaviors. This data is essential for market analysts, financial institutions, and businesses aiming to optimize strategies and enhance customer experiences. Two primary categories exist within this data segment: credit card transactions and debit card transactions. Credit card transactions reveal consumers' discretionary spending patterns, luxury purchases, and credit management abilities. By analyzing this data through quantitative methods, such as regression analysis and time series analysis, businesses can gain a deeper understanding of consumer preferences and trends. Debit card transactions, on the other hand, provide insights into essential spending habits, budgeting strategies, and daily expenses. This data is crucial for understanding consumers' practical needs and lifestyle choices. Machine learning algorithms, such as deep learning and predictive analytics, can be employed to uncover patterns and trends in debit card transactions, enabling businesses to tailor their offerings and services accordingly. Data governance, data security, and data accuracy are critical considerations when dealing with sensitive financial d

This dataset supports a research project in the field of digital medicine, which aims to quantify the impact of disseminating scientific information on social media—as a form of "meta-intervention"—on public adherence to Non-Pharmaceutical Interventions (NPIs) during health crises such as the COVID-19 pandemic. The research encompasses multiple sub-studies and pilot experiments, drawing data from various global and China-specific social media platforms.The data included in this submission has been collected from several sources:From Sina Weibo and Tencent WeChat, 189 online poll datasets were collected, involving a total of 1,391,706 participants. These participants are users of Sina Weibo or Tencent WeChat.From Twitter, 187 tweets published by scientists (verified with a blue checkmark) related to COVID-19 were collected.From Xiaohongshu and Bilibili, textual content from 143 user posts/videos concerning COVID-19, along with associated user comments and specific user responses to a question, were gathered.It is important to note that while the broader research project also utilized a 3TB Reddit corpus hosted on Academic Torrents (academictorrents.com), this specific Reddit dataset is publicly available directly from Academic Torrents and is not included in this particular DataHub submission. The submitted dataset comprises publicly available data, formatted as Excel files (.xlsx), and includes the following:Filename: scientists' discourse (source from screenshot of tweets)Description: This file contains screenshots of tweets published by scientists on Twitter concerning COVID-19 research, its current status, and related topics. It also includes a coded analysis of the textual content from these tweets. Specific details regarding the coding scheme can be found in the readme.txt file.Filename: The links of online polls (Weibo & WeChat)Description: This data file includes information from online polls conducted on Weibo and WeChat after December 7, 2022. These polls, often initiated by verified users (who may or may not be science popularizers), aimed to track the self-reported proportion of participants testing positive for COVID-19 (via PCR or rapid antigen test) or remaining negative, particularly during periods of rapid Omicron infection spread. The file contains links to the original polls, links to the social media accounts that published these polls, and relevant metadata about both the poll-creating accounts and the online polls themselves.Filename: Online posts & comments (From Xiaohongshu & Bilibili)Description: This file contains textual content from COVID-19 related posts and videos published by users on the Xiaohongshu and Bilibili platforms. It also includes user-generated comments reacting to these posts/videos, as well as user responses to a specific question posed within the context of the original content.Key Features of this Dataset:Data Type: Mixed, including textual data, screenshots of social media posts, web links to original sources, and coded metadata.Source Platforms: Twitter (global), Weibo/WeChat (primarily China), Xiaohongshu (China), and Bilibili (video-sharing platform, primarily China).Use Case: This dataset is intended for the analysis of public discourse, the dissemination of scientific information, and user engagement patterns across different cultural contexts and social media platforms, particularly in relation to public health information.

Objective: Driven by the need of pharmacovigilance centres and companies to routinely collect and review all available data about adverse drug reactions (ADRs) and adverse events of interest, we introduce and validate a computational framework exploiting dominant as well as emerging publicly available data sources for drug safety surveillance. Methods: Our approach relies on appropriate query formulation for data acquisition and subsequent filtering, transformation and joint visualization of the obtained data. We acquired data from the FDA Adverse Event Reporting System (FAERS), PubMed and Twitter. In order to assess the validity and the robustness of the approach, we elaborated on two important case studies, namely, clozapine-induced cardiomyopathy/myocarditis versus haloperidol-induced cardiomyopathy/myocarditis, and apixaban-induced cerebral hemorrhage. Results: The analysis of the obtained data provided interesting insights (identification of potential patient and health-care professional experiences regarding ADRs in Twitter, information/arguments against an ADR existence across all sources), while illustrating the benefits (complementing data from multiple sources to strengthen/confirm evidence) and the underlying challenges (selecting search terms, data presentation) of exploiting heterogeneous information sources, thereby advocating the need for the proposed framework. Conclusions: This work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

This assessment evaluates 34 internet activity data sources for use in disease surveillance. Our goals are to (a) understand the available data on internet usage and activity well enough to (b) identify real-world internet data sources that can be used both for evaluating our theories of disease surveillance and buildingoperational internet data-based disease surveillance systems.The assessment (pdf) and raw data (excel spreadsheet) are attached.

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, although there is rapid uptake among older age groups. 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 last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. 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 have 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. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, 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 rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

Data Visualization Market Outlook

The global data visualization market size was valued at approximately USD 6.5 billion in 2023 and is projected to reach USD 19.8 billion by 2032, growing at a robust CAGR of 12.8% during the forecast period. This impressive growth can be attributed to the escalating need for organizations to make data-driven decisions, the proliferation of big data, and the increasing adoption of advanced analytics tools.

One of the primary growth factors driving the data visualization market is the exponential increase in data generation across various industries. With the advent of IoT, social media proliferation, and digital transformation, organizations are inundated with vast amounts of data. The need to interpret this data to derive meaningful insights has never been greater. Data visualization tools enable businesses to transform raw data into graphical representations, facilitating easier understanding and more informed decision-making.

Another significant growth driver is the increasing adoption of business intelligence (BI) and analytics solutions. Enterprises are progressively recognizing the value of BI tools in gaining competitive advantages. Data visualization is a critical component of these BI platforms, providing interactive and dynamic representations of data that can be manipulated to uncover trends, patterns, and correlations. This ability to visualize complex data sets enhances strategic planning and operational efficiencies.

The rising demand for personalized customer experiences is also contributing to market growth. In sectors like retail, BFSI, and healthcare, understanding customer behavior and preferences is paramount. Data visualization tools help organizations analyze customer data in real-time, enabling them to tailor offerings and improve customer engagement. The ability to visualize data in an intuitive manner accelerates the speed at which businesses can respond to market changes and customer needs.

Marketing Dashboards have become an essential tool for businesses seeking to optimize their marketing strategies through data visualization. These dashboards provide a comprehensive view of marketing performance by aggregating data from various sources such as social media, email campaigns, and web analytics. By presenting this data in an easily digestible format, marketing teams can quickly identify trends, track campaign effectiveness, and make informed decisions to enhance their marketing efforts. The ability to customize these dashboards allows organizations to focus on key performance indicators that are most relevant to their objectives, ultimately leading to more targeted and successful marketing initiatives.

From a regional perspective, North America holds a significant share of the data visualization market, driven by the presence of major technology providers and high adoption rates of advanced analytics tools. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is fueled by the increasing digitalization initiatives, rising investments in IT infrastructure, and the growing awareness of data-driven decision-making in emerging economies such as India and China.

Component Analysis

The data visualization market comprises two primary components: software and services. The software segment is further categorized into standalone visualization tools and integrated data visualization solutions. Standalone visualization tools are designed specifically for data visualization purposes, offering features such as interactive dashboards, real-time analytics, and customizable visualizations. Integrated solutions, on the other hand, are part of larger business intelligence or analytics platforms, providing seamless integration with other data management and analysis tools.

The services segment includes consulting, implementation, and support services. Consulting services help organizations identify the right data visualization tools and strategies to meet their specific needs. Implementation services ensure the successful deployment and integration of visualization solutions within the existing IT infrastructure. Support services provide ongoing maintenance, updates, and troubleshooting to ensure the smooth functioning of the data visualization tools.

Within the software segment, the demand for cloud-based data visualization solutions is growing rapidly. Cloud

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.