In the second quarter of 2022, X/Twitter's revenue amounted to over 1.17 billion U.S. dollars. The platform's majority of the social network’s revenues are generated via advertising, followed by data licensing and other revenues.

X/Twitter company information X/Twitter ranks among the biggest social networks worldwide. As of the second quarter of 2021, the company reported 206 million monetizable daily active users worldwide. In 2020, Twitter’s annual revenue came to 3.72 billion U.S. dollars. However, the company has only been able to report a positive net income in 2018 and 2019, revealing a struggle to monetize users.

X/Twitter marketing and advertising Social media advertising spending in the United States is constantly growing. In 2019, the social media ad spend per U.S. internet user amounted to 129.1 U.S. dollars and this figure is projected to sharply increase to over 174 U.S. dollars by 2024. Along with Instagram, X/Twitter is a popular marketing channel and some brands and companies have made a name for themselves for being entertaining on Twitter.

In the last reported quarter, social network X/Twitter had a net loss of 270 million U.S. dollars, down from a net income of 513 million U.S. dollars in the first quarter of 2022.In 2020, the decline in revenue was due to an advertising slump caused by the outbreak of the global coronavirus pandemic, which led the company to recognize a deferred tax asset valuation allowance of 1.1 billion U.S. and a non-cash income tax expense based primarily on cumulative taxable losses driven primarily by COVID-19.

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

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.

In 2021, Twitter generated more than 4.5 billion U.S. dollars in advertising service revenues, up from 3.2 billion U.S. dollars in the previous year. Additionally, the micro-blogging platform produced around 571 million U.S. dollars in data licensing revenue, up from 508 million U.S. dollars in 2020.

In the second quarter of 2022, X (formerly Twitter) generated total advertising revenues of approximately one billion U.S. dollars, down from 1.1 billion U.S. dollars in the previous quarter. Advertising accounts for the vast majority of the social network's revenues, while data licensing and other revenue for the second quarter of 2022 amounted to 100 million U.S. dollars. X/Twitter experienced overall net losses of 270 million U.S. dollars during this quarter.

These are the key Twitter user statistics that you need to know.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

In 2021, X's (formerly Twitter) annual net loss amounted to *** million U.S. dollars. Overall, this is a significant decrease from the previous year, in which the micro blogging and social network company saw losses of almost *** billion U.S. dollars.

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their topic.

The dataset holds 21,107 documents annotated with 20 labels:

topics = { "LABEL_0": "Analyst Update", "LABEL_1": "Fed | Central Banks", "LABEL_2": "Company | Product News", "LABEL_3": "Treasuries | Corporate Debt", "LABEL_4": "Dividend"… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic.

Introduction

X Statistics: X (formerly called Twitter) is one of the most popular social media platforms globally. Furthermore, it consistently ranks among the top websites that are visited regularly. Likewise, short posts like tweets have become synonymous with this platform. Since Elon Musk acquired this platform, it has become an alternative to legacy news media.

Going through the X (formerly Twitter) statistics makes it possible to garner essential information that has contributed to this platform's immense popularity and learn how it works in the dynamic world of social media.

This is the breakdown of Twitter users by age group.

The revenue of Twitter, Inc. with headquarters in the United States amounted to **** billion U.S. dollars in 2021. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2017 this is a total increase by approximately **** billion U.S. dollars. The trend from 2017 to 2021 shows, furthermore, that this increase happened continuously.

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.

@lis_grievances 5 year tweet archives. Covers the period of February 26, 2016 to February 27, 2021. Project homepage: https://lisgrievances.com Featured in: “Put the Fucking Salary in the Job Ad”: An Analysis of an Anonymous Corpus of Tweets by Tim Ribaric (ORCID ID: 0000-0001-9229-8569) Preprint: http://hdl.handle.net/10464/16867 Found in: Libraries as Dysfunctional Organizations and Workplaces. (ISBN: 9781003159155) DOI: 10.4324/9781003159155-8 This is an enriched export of the Twitter archive (https://help.twitter.com/en/managing-your-account/how-to-download-your-twitter-archive) of @lis_grievances with derived columns of extra data. Data is anonymized.

The platform is male-dominated with 68.1% of all Twitter users being male. Just 31.9% of Twitter users are female.

With over 611 million monthly active users, building a huge Twitter following is not an easy task. These are the top 25 accounts with the most followers on Twitter right now.

Context

On 25 March,2019 Indian National Congress president Rahul Gandhi announced the Nyuntam Aay Yojana. Nyuntam Aay Yojana (Minimum Income Scheme) or, in short, NYAY, an acronym that also spells out “justice” in Hindi. The plan is to give ₹6,000 every month to the bottom 20% of the population of around 5 crore families or 25 crore individuals. NYAY will be by far the biggest basic income scheme anywhere in the world.

Content

The data contains 9999 most recent tweets on #Minimum Income Scheme. There are 9999 rows(one for each tweet) and 12 columns.

Rows: 9999

Columns: 1.Created-At 2.From-User 3.From-User-Id 4.To-User 5.To-User-Id 6.Language 7.Source 8.Text 9.Geo-Location-Latitude 10.Geo-Location-Longitude 11.Retweet-Count 12.Id

Acknowledgements

The data was collected using the "searchTwitteR" in RapidMiner using the twitter API. Dataset is created by pulling tweets by hashtag(#Minimum Income Scheme) from twitter.

Inspiration

What percentage of tweets are negative, positive or neutral ? What is the general sentiment of tweets ? Conclusion regarding tweet sentiments. An in depth exploratory analysis of data.

The collected tweets and Earnings Announcements (EA) cover the period of three years, from June 1, 2013 to June 3, 2016. Companies are ordered by the total number of tweets collected. For each company, there is the sentiment distribution, market capitalization, and the prevailing timing of EAs with respect to the NYSE trading hours. Each company issues four EAs per year, therefore there is a total of 360 EAs (30 companies, three years, four EAs per year)1.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio