In the most recent reported year, X (formerly Twitter) spent around 1.17 billion U.S. dollars on sales and marketing, up from 887 million U.S. dollars in the previous year. The social networking platform generated a worldwide revenue of over five billion U.S. dollars in 2021.

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like South America and the Americas.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

These Twitter user statistics will give you the complete story of where Twitter is at today and what the future looks like for the social media company.

In 2022, X (formerly Twitter) generated **** billion U.S. dollars in advertising revenue. This figure is expected to drop to *** billion U.S. dollars by 2027. The social platform is responsible for roughly one percent of the global ad revenue. Social network platform in transition Established in 2006, California-based platform X (formerly known as Twitter) ranked among the most popular social networks worldwide by number of monthly users as of late 2023. The company has undergone a profound transformation after Elon Musk's acquisition in October 2022. After the billionaire bought the platform, advertisers and marketers have closely observed every change regarding its features and policies. Due to many contradictory developments at Twitter, industry professionals have become increasingly more cautious with their investments. A prominent example was IPG - one of the world's top five advertisers - advising its customers to temporarily abstain from putting money into the platform. Another big advertiser who has also ceased ad investment in Walmart. Social media powerhouse: Facebook In the meantime, nearly half of the responding marketing professionals named Facebook the most important social media platform for their business. The social network giant's revenue amounted to *** billion dollars in 2022. Although the figure was roughly ** percent of Google's ad revenue, Meta stood further ahead of X in terms of money earned through selling ad spaces.

Demographic data prediction is powered by Demografy AI that extracts demographic data from names with 100% coverage, accuracy preview before purchase and GDPR-compliance.

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Use cases: - Social Media analytics and user segmentation - Competitor analysis - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

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Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You need only names of social media users. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

We aggregated a Twitter dataset utilizing Twitter Archiving Google Sheet (TAGS) to interact with Twitter’s API and return relevant data. To analyze the marketing side of the conversation around ChatGPT, we selected #ChatGPT as a common hashtag to target tweets talking about AI. This is the marketing dataset, so we included hashtags “marketing”, “content creation”, or “creator economy” as content creation is a field heavily impacted by ChatGPT’s writing capabilities as a chatbot and creator economy is a common word used by experts to describe the overarching industry. This would give us a more specific dataset to analyze what people well-versed in marketing, ChatGPT’s ideal audience, thought about AI’s role in marketing. Because of the TAGS limitation, our dataset was limited to tweets ranging from January 21st to January 25th for both datasets.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of September, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android for the standard subscription. Twitter Blue costs between three and 40 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures.

A 3-month sample of brand and consumer tweets from Top brands on Twitter. This data contains marketing messages, metadata in the form of eWOM outcomes (Like, Share, Comment) and an explicit structure extracted from content which is based on value propositions. Brand and consumer value propositions, sentiments and eWOM outcomes can then be quantitatively analysed for underlying statistical relationships

The data set is a collection of tweets extracted from Twitter for two specific platforms. It is date bound.

This data sets consists of all the tweets collected and segregated after extraction on the theme of digital and technology enabled health. This data set has been analysed with Twitter analytics for establishing the propositions highlighted in our study.

In 2021, Twitter accounted for *** percent of the global digital advertising revenue. After its acquisition by Elon Musk, the share was expected to fall to *** percent in 2022 and remain stable in 2023. In 2024, the value was projected to fall further to *** percent.

This dataset is about book subjects. It has 2 rows and is filtered where the books is The complete idiots guide to Twitter marketing. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Social media is an important channel for communication, information dissemination, and social interaction, but also provides opportunities to illicitly sell goods online, including the trade of wildlife products. In this study, we use the Twitter public application programming interface (API) to access Twitter messages in order to detect and classify suspicious wildlife trafficking and sale using an unsupervised machine learning topic model combined with keyword filtering and manual annotation. We choose two prohibited wildlife animals and related products: elephant ivory and pangolin, and collected tweets containing keywords and known code words related to these species. In total, we collected 138,357 tweets filtered for these keywords over a 14-day period and were able to identify 53 tweets from 38 unique users that we suspect promoted the sale of Ivory products, though no pangolin related promoted post were detected in this study. Study results show that machine learning combined with supplement analysis approaches such as those utilized in this study have the potential to detect illegal content without the use of an existing training data set. If developed further, these approaches can help technology companies, conservation groups, and law enforcement officials to expedite the process of identifying illegal online sales and stem supply for the billion-dollar criminal industry of online wildlife trafficking.

Twitter's ad business shrank both in 2023 and 2024, with global ad revenues falling to two billion dollars, according to the source's estimates. After the acquisition of Twitter by Elon Musk on October 27, 2022, the platform saw an exodus of users. December 2022 projections saw more than 30 million users leaving the platform by the end of 2024.

During a global 2022 survey, roughly **** percent of responding marketers stated they did not use Twitter for business purposes. Among the remaining ** percent that did use the platform, the largest share - approximately ** percent - had been using it between *** and three years.

This database contains information (text, likes etc.) about the messages published on Twitter by the NGOs working for children selected for this research from from November 19th, 2022, to March 31st, 2023. This time frame was selected based on International Children’s Day, celebrated on November 20th.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Elon Musk's Twitter Dream in Free Fall

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

IntroductionOn October 12, 2021, the FDA issued its first marketing granted orders for Vuse, the e-cigarette product by R.J. Reynolds Vapor Company. The public perceptions and reactions to the FDA’s Vuse authorization are prevalent on social media platforms such as Twitter/X. We aim to understand public perceptions of the FDA’s Vuse authorization in the US using Twitter/X data.MethodsThrough the Twitter/X streaming API (Application Programming Interface), 3,852 tweets between October 12, 2021, and October 23, 2021, were downloaded using the keyword of Vuse. With the elimination of retweets, irrelevant tweets, and tweets from other countries, the final dataset consisted of 523 relevant tweets from the US. Based on their attitudes toward the FDA authorization on Vuse, these tweets were coded into three major categories: positive, negative, and neutral. These tweets were further manually classified into different categories based on their contents.ResultsThere was a large peak on Twitter/X mentioning FDA’s Vuse authorization on October 13, 2021, just after the authorization was announced. Of the 523 US tweets related to FDA’s Vuse authorization, 6.12% (n=32) were positive, 26.77% (n=140) were negative, and 67.11% (n=351) were neutral. In positive tweets, the dominant subcategory was Cessation Claims (n=18, 56.25%). In negative tweets, the topics Health Risk (n=43, 30.71%), Criticize Authorization (n=42, 30.00%), and Big Tobacco (n=40, 38.57%) were the major topics. News (n=271, 77.21%) was the most prevalent topic among neutral tweets. In addition, tweets with a positive attitude tend to have more likes.DiscussionPublic perceptions and discussions on Twitter/X regarding the FDA’s Vuse authorization in the US showed that Twitter/X users were more likely to show a negative than a positive attitude with a major concern about health risks.

Social Media Data for European Companies offers a powerful tool for organizations looking to enhance their decision-making through informed strategies. By providing links to social media profiles across various platforms—including LinkedIn, Facebook, Instagram, X (formerly Twitter), YouTube, TikTok, and GitHub—this solution caters to the specific needs of industries ranging from sales to recruitment. Updated monthly and fully compliant with GDPR regulations, it ensures reliability, relevancy, and trustworthiness.

LinkedIn – A leading network for businesses and professionals, ideal for B2B interactions.

Facebook – A hub for business pages, reviews, and customer engagement.

Instagram – A visually-driven platform for brand marketing and audience engagement.

X (formerly Twitter) – Known for real-time updates and customer interactions.

YouTube – A video powerhouse offering in-depth brand storytelling opportunities.

TikTok – A rapidly growing platform for creative and viral content.

GitHub – A crucial resource for tech professionals and organizations focused on open-source projects.