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.

As of February 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 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 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usage Currently, 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 and friends. Global impact of social media Social 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 polarization in politics, and heightened everyday distractions.

How many people use social media?

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

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

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

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

How Does Daily Yoga Impact Screen Time Habits

A Study of Daily Screen Time Behavior

By Taylor L Bailey [source]

About this dataset

This dataset contains data on daily minutes of screen time between April 17th and May 14th. With this dataset, you can gain insights into daily phone usage habits and determine the effect that regular yoga practice has on reducing phone use. By recording the amount of time spent using different types of apps -- such as social media, reading, productivity and entertainment -- you can understand how phone habits have changed over time. Moreover, this dataset captures my attempt to do at least 10 minutes of yoga every day for a period of 15 days from April 29th to May 13th. Did this experiment successfully reduce my screen time overall? Dive in deep and find out!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to use this dataset

This dataset contains information on daily minutes of screen time habits, categorized by type of usage, as well as the effect of yoga on those habits. This is useful for gaining insights into an individual's screen time habits and its variability with respect to doing yoga.

To start with, there are a few key columns to check out: Date (to keep track of the days in view), Week Day (to identify which day it is precisely), Social Networking/Reading and Reference/Other/Productivity/Health and Fitness (to determine how much time was spent in each category) and Yoga (whether or not any yoga was done that day).

You may find it helpful to analyze the daily data over a certain duration by creating separate datasets grouped by weeks or months. Additionally, tallying each person's total minutes per week or per month can show changes over long-term periods. As you will notice right away in viewing this dataset, consistency is important; if someone were tracking their smartphone use regularly but only measured twice during a month period or skipped days without setting aside any reference points prior, then this particular experiment would be somewhat difficult to draw conclusions from. It would be especially impactful if specific factors such as sleep hygiene were tracked along with practice evolution such us advanced yoga sequences tried out over time alongside different approaches at making screens off-limits during mealtime - all items that could bring interesting insight into our relationship with technology devices when looking at screentime fluctuations before and after our mediations become part of our daily routine

Research Ideas

• Track the impact of daily yoga on overall and category-specific screen time.
• Explore the relationship between day of the week and overall or category-specific screen time.
• Investigate how long it takes to establish a healthy habit, such as decreased phone usage, by looking at changes in average daily screen time over the period of a month or two months before and after beginning yoga practice, adjusting for weekly period effect

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Screen Time Data.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------| | Date | The date of the data entry. (Date) | | Week Day | The day of the week of the data entry. (String) | | Social Networking | The amount of time spent on social networking. (Integer) | | Reading and Reference | The amount of time spent on reading and reference activities. (Integer) | | Other ...

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

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

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

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

Social Media has become a part of our day-to-day routine, keeping users from across the world well-connected through digital platforms. With each passing year, social media is evolving at a rapid speed. With each passing year, the number of social media users is increasing at an immersive speed. Reports also suggest the number of social media users will reach a milestone of 5.85 billion in 2027.

In 2024, 62.6% of the world’s population will access social media, which clearly indicates the dominance of social media platforms in today’s world. In this article, we will examine social media statistics for 2024, uncovering monthly active users, daily time spent by users, most downloaded social media apps, etc.

A global survey conducted in the third quarter of 2024 found that the main reason for using social media was to keep in touch with friends and family, with over 50.8 percent of social media users saying this was their main reason for using online networks. Overall, 39 percent of social media users said that filling spare time was their main reason for using social media platforms, whilst 34.5 percent of respondents said they used it to read news stories. Less than one in five users were on social platforms for the reason of following celebrities and influencers.

              The most popular social network

              Facebook dominates the social media landscape. The world's most popular social media platform turned 20 in February 2024, and it continues to lead the way in terms of user numbers. As of February 2025, the social network had over three billion global users. YouTube, Instagram, and WhatsApp follow, but none of these well-known brands can surpass Facebook’s audience size.
              Moreover, as of the final quarter of 2023, there were almost four billion Meta product users.

              Ever-evolving social media usage

              The utilization of social media remains largely gratuitous; however, companies have been encouraging users to become paid subscribers to reduce dependence on advertising profits. Meta Verified entices users by offering a blue verification badge and proactive account protection, among other things. X (formerly Twitter), Snapchat, and Reddit also offer users the chance to upgrade their social media accounts for a monthly free.

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Preprint

Abstract

The World Wide Web is a complex interconnected digital ecosystem, where information and attention flow between platforms and communities throughout the globe. These interactions co-construct how we understand the world, reflecting and shaping public discourse. Unfortunately, researchers often struggle to understand how information circulates and evolves across the web because platform-specific data is often siloed and restricted by linguistic barriers. To address this gap, we present a comprehensive, multilingual dataset capturing all Wikipedia links shared in posts and comments on Reddit from 2020 to 2023, excluding those from private and NSFW subreddits. Each linked Wikipedia article is enriched with revision history, page view data, article ID, redirects, and Wikidata identifiers. Through a research agreement with Reddit, our dataset ensures user privacy while providing a query and ID mechanism that integrates with the Reddit and Wikipedia APIs. This enables extended analyses for researchers studying how information flows across platforms. For example, Reddit discussions use Wikipedia for deliberation and fact-checking which subsequently influences Wikipedia content, by driving traffic to articles or inspiring edits. By analyzing the relationship between information shared and discussed on these platforms, our dataset provides a foundation for examining the interplay between social media discourse and collaborative knowledge consumption and production.

Datasheet

Motivation

The motivations for this dataset stem from the challenges researchers face in studying the flow of information across the web. While the World Wide Web enables global communication and collaboration, data silos, linguistic barriers, and platform-specific restrictions hinder our ability to understand how information circulates, evolves, and impacts public discourse. Wikipedia and Reddit, as major hubs of knowledge sharing and discussion, offer an invaluable lens into these processes. However, without comprehensive data capturing their interactions, researchers are unable to fully examine how platforms co-construct knowledge. This dataset bridges this gap, providing the tools needed to study the interconnectedness of social media and collaborative knowledge systems.

Composition

WikiReddit, a comprehensive dataset capturing all Wikipedia mentions (including links) shared in posts and comments on Reddit from 2020 to 2023, excluding those from private and NSFW (not safe for work) subreddits. The SQL database comprises 336K total posts, 10.2M comments, 1.95M unique links, and 1.26M unique articles spanning 59 languages on Reddit and 276 Wikipedia language subdomains. Each linked Wikipedia article is enriched with its revision history and page view data within a ±10-day window of its posting, as well as article ID, redirects, and Wikidata identifiers. Supplementary anonymous metadata from Reddit posts and comments further contextualizes the links, offering a robust resource for analysing cross-platform information flows, collective attention dynamics, and the role of Wikipedia in online discourse.

Collection Process

Data was collected from the Reddit4Researchers and Wikipedia APIs. No personally identifiable information is published in the dataset. Data from Reddit to Wikipedia is linked via the hyperlink and article titles appearing in Reddit posts.

Preprocessing/cleaning/labeling

Extensive processing with tools such as regex was applied to the Reddit post/comment text to extract the Wikipedia URLs. Redirects for Wikipedia URLs and article titles were found through the API and mapped to the collected data. Reddit IDs are hashed with SHA-256 for post/comment/user/subreddit anonymity.

Uses

We foresee several applications of this dataset and preview four here. First, Reddit linking data can be used to understand how attention is driven from one platform to another. Second, Reddit linking data can shed light on how Wikipedia's archive of knowledge is used in the larger social web. Third, our dataset could provide insights into how external attention is topically distributed across Wikipedia. Our dataset can help extend that analysis into the disparities in what types of external communities Wikipedia is used in, and how it is used. Fourth, relatedly, a topic analysis of our dataset could reveal how Wikipedia usage on Reddit contributes to societal benefits and harms. Our dataset could help examine if homogeneity within the Reddit and Wikipedia audiences shapes topic patterns and assess whether these relationships mitigate or amplify problematic engagement online.

Distribution

The dataset is publicly shared with a Creative Commons Attribution 4.0 International license. The article describing this dataset should be cited: https://doi.org/10.48550/arXiv.2502.04942

Maintenance

Patrick Gildersleve will maintain this dataset, and add further years of content as and when available.

SQL Database Schema

Table: posts

Table: comments

Table: postlinks

Table: commentlinks

China Retail Investor Sentiment Analytics provides sentiment analytics of Chinese retail investors based on 2 stock forums, Guba (GACRIS dataset) and Xueqiu (XACRIS dataset), the most popular stock forums in China from 2007.

By utilizing in-house NLP models which are dedicatedly optimized for Chinese stock forum posts and trained on a proprietary manually labeled and cross-checked training data, the dataset provides accurate text analytics of post content, including but not limited to quality, sentiment, and relevant stocks with relevance score. In addition to the aggregated statistics of stock sentiment and popularity, the dataset also provides rich and fine-grained information for each user/post in record level. For example, it reports the registration time, number of followers for each user, and also the replies/readings and province being published for each post. Moreover, these meta data are processed in point-in-Time (PIT) manner since 2019.

The dataset could help clients easily capture the sentiment and popularity among millions of Chinese retail investors. On the other hand, it also offers flexibility for clients to customize novel analytics, such as studying the sentiment (conformity/divergence) of users of different level of influence or posts of different hotness, or simply filtering the posts published by users which are too active/positive/negative in a time window when aggregating the statistics.

Coverage: All A-share and Hong Kong stocks, 300+ popular US stocks Update Frequency: Daily or intra-day

Data Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com .

Citation

Please cite our work as

@article{shahi2021overview,
 title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
 author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
 journal={Working Notes of CLEF},
 year={2021}
}

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English.

Subtask 3A: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:

• False - The main claim made in an article is untrue.

• Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

• True - This rating indicates that the primary elements of the main claim are demonstrably true.

• Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Subtask 3B: Topical Domain Classification of News Articles (English) Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

Task 3a

• ID- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• our rating - class of the news article as false, partially false, true, other

Task 3b

• public_id- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• domain - domain of the given news article(applicable only for task B)

Output data format

Task 3a

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source:

• Fakenews Classification Datasets
• Fake News Detection Challenge KDD 2020
• FakeNewsNet

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.

Evaluation Metrics

This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.

Submission Link: https://competitions.codalab.org/competitions/31238

Related Work

• Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
• G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
• Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

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 July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 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. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

The ClimateCheck Dataset

This dataset is used for the ClimateCheck: Scientific Fact-checking of Social Media Posts on Climate Change Shared Task hosted at the Scholarly Document Processing workshop at ACL 2025. The shared task is hosted on Codabench. Claims The claims used for this dataset were gathered from the following existing resources: ClimaConvo, DEBAGREEMENT, Climate-Fever, MultiFC, and ClimateFeedback. Some of which are extracted from social media (Twitter/X and Reddit)… See the full description on the dataset page: https://huggingface.co/datasets/rabuahmad/climatecheck.

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

During a 2024 survey among marketers worldwide, approximately 83 percent selected increased exposure as a benefit of social media marketing. Increased traffic followed, mentioned by 73 percent of the respondents, while 65 percent cited generated leads.

              The multibillion-dollar social media ad industry

              Between 2019 – the last year before the pandemic – and 2024, global social media advertising spending skyrocketed by 140 percent, surpassing an estimated 230 billion U.S. dollars in the latter year. That figure was forecast to increase by nearly 50 percent by the end of the decade, exceeding 345 billion dollars in 2029. As of 2024, the social media networks with the most monthly active users were Facebook, with over three billion, and YouTube, with more than 2.5 billion.

              Pros and cons of GenAI for social media marketing

              According to another 2024 survey, generative artificial intelligence's (GenAI) leading benefits for social media marketing according to professionals worldwide included increased efficiency and easier idea generation. The third place was a tie between increased content production and enhanced creativity. All those advantages were cited by between 33 and 38 percent of the interviewees. As for GenAI's top challenges for global social media marketing,
              maintaining authenticity and the value of human creativity ranked first, mentioned by 43 and 40 percent of the respondents, respectively. Another 35 percent deemed ensuring the content resonates as an obstacle.

Data Definition

campaign_item_id : unique id of each adevertising campaign no_of_days : number of days campaign has been running time : timestamp on which the data was captured ext_service_id : id of each advertising platforms used ext_service_name : name of each advertising platforms used creative_id : id of the creative images used for ads creative_height : height of the creative image for the ad in pixels creative_width : width of the creative image for the ad in pixels search_tags : search tags used for displaying ads template_id : template used in the creative image landing_page : landing page url on which users clicked or browsed through advertiser_id : id of the advertiser advertiser_name : name of the place of the advertiser ( city , country , state ) network_id : id of the each agency advertiser_currency : currency of the country in which the advertiser operates in channel_id : id of each channel used for placed ads channel_name : name of the channel ( display , search , social , mobile video ) max_bid_cpm : maximum value of bid for optimizing cpm campaign_budget_usd : overall budget of the campaign or the amount of money that the campaign can spend impressions : the number of times an advertisement is displayed on a website or social media platform. clicks : the number of times an advertisement is clicked on by a user, leading them to the advertiser's website or landing page. currency_code : the currency code of the advertiser exchange_rate : a relative price of one currency expressed in terms of another currency. media_cost_usd : the amount of money that the campaign has spent on that particuar day position_in_content : position where the ad was placed on the website page unique_reach : the number of unique users who see your post or page. total_reach : the number of people who saw any content from your page or about your page. search_tags : a word or set of words a person enters when searching on Google or one of our Search Network sites. cmi_currency_code : campaign currency code time_zone : timezone in which the campaign is running weekday_cat : weekday / weekend catgeory keywords : a word or set of words that Google Ads advertisers can add to a given ad group so that your ads are targeting the right audience.

🌟 Enjoying the Dataset? 🌟

If this dataset helped you uncover new insights or make your day a little brighter. Thanks a ton for checking it out! Let’s keep those insights rolling! 🔥📈

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23961675%2Ff3761bd2d7ee460ad464de8f25634f63%2Fsteve-johnson-z6LlNgsDeug-unsplash.jpg?generation=1740481184467263&alt=media" alt="">

Dataset Description:

This dataset contains website conversion data for Bluetooth speaker sales. The dataset tracks user sessions on different landing page variants, with the primary goal of analyzing conversion rates, user behavior, and other factors influencing sales. It includes detailed user engagement metrics such as time spent, pages visited, device type, sign-in methods, and geographical information.

Use Case:

This dataset can be used for various analytical tasks including:

A/B testing and multivariate analysis to compare landing page designs.
User segmentation by demographics (age, gender, location, etc.).
Conversion rate optimization (CRO) analysis.
Predictive modeling for conversion likelihood based on session characteristics.
Revenue and payment analysis.

eBackgroundThe Digital Humanities 2016 conference is taking/took place in Kraków, Poland, between Sunday 11 July and Saturday 16 July 2016. #DH2016 is/was the conference official hashtag.What This Output IsThis is a CSV file containing a total of 4175 Tweets publicly published with the hashtag #DH2016 on Wednesday 13 July 2016 GMT.The archive starts with a Tweet published on Wednesday July 13 2016 at 00:31:16 +0000 and ends with a Tweet published on Wednesday July 13 2016 at 23:58:33 +0000. Previous days have been shared on a different output. A breakdown of Tweets per day so far:Sunday 10 July 2016: 179 TweetsMonday 11 July 2016: 981 TweetsTuesday 12 July 2016: 2318 TweetsWednesday 13 July 2016: 4175 Tweets Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. Only users with at least 1 follower were included in the archive. Retweets have been included (Retweets count as Tweets). The collection spreadsheet was customised to reflect the time zone and geographical location of the conference.The profile_image_url and entities_str metadata were removed before public sharing in this archive. Please bear in mind that the conference hashtag has been spammed so some Tweets colllected may be from spam accounts. Some automated refining has been performed to remove Tweets not related to the conference but the data is likely to require further refining and deduplication. Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet tagged with #dh2016 during the indicated period, and the dataset is shared for archival, comparative and indicative educational research purposes only.Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. No private personal information is shared in this dataset. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road. This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter. Other ConsiderationsTweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question.The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time.The CC-BY license has been applied to the output in the repository as a curated dataset. Authorial/curatorial/collection work has been performed on the file in order to make it available as part of the scholarly record. The data contained in the deposited file is otherwise freely available elsewhere through different methods and anyone not wishing to attribute the data to the creator of this output is needless to say free to do their own collection and clean their own data.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.