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.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

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.

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.

Please cite the following paper when using this dataset: N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292 Abstract The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post into one of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral hate or not hate anxiety/stress detected or no anxiety/stress detected These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications. The distinct languages in which Instagram posts are present in this dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian The following is a description of the attributes present in this dataset: Post ID: Unique ID of each Instagram post Post Description: Complete description of each post in the language in which it was originally published Date: Date of publication in MM/DD/YYYY format Language: Language of the post as detected using the Google Translate API Translated Post Description: Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts. Sentiment: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutral Hate: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hate Anxiety or Stress: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected. All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

Post ID: Unique ID of each Instagram post

Post Description: Complete description of each post in the language in which it was originally published

Date: Date of publication in MM/DD/YYYY format

Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API

Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API

Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

How does sentiment toward COVID-19 vary across different languages?

How has public sentiment toward COVID-19 evolved from 2020 to the present?

How do cultural differences affect social media discourse about COVID-19 across various languages?

How has COVID-19 impacted mental health, as reflected in social media posts across different languages?

How effective were public health campaigns in shifting public sentiment in different languages?

What patterns of vaccine hesitancy or support are present in different languages?

How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?

What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?

How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?

What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

This dataset covers aspects of online politics in 25 democracies: 15 relatively old established European democracies (Austria, Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, United Kingdom); five non-European veteran democracies (Australia, Canada, Israel, Japan, New Zealand); two early (Portugal, Spain) and three late (Czech Republic, Hungary, Poland) third-wave (young) European democracies. The research population includes, in each country, parties that won 4% or more of the votes in two consecutive elections before April 2019 (a total of 141 parties and 145 leaders). The dataset includes external party level information such as performance in the last national elections, governmental status, party age, populism affiliation and leadership selection method. It also includes information related to the party leaders such as their term in leadership office and other formal positions. In addition it includes information about online activity mainly on the consumption (user related activities) of the parties and their leaders in Facebook and Twitter two of the most used social media platforms for political purposes.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, 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 and count multiple accounts by persons only once.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).

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

This data set contains all gather information of the MyMovez project, which investigated adolescents’ health behaviors (ie., nutrition, media use, and physical activity) and their social networks for three years. The first year (2016; data collection waves 1, 2, 3) and the second year (2017; wave 4) marked the first phase of the project in which the health behaviors of adolescents were monitored without intervening. The third year (waves 5, 6, 7) marked the second phase of the project in which four different types of interventions were tested to promote either water consumption or physical activity. A fifth group did not receive an intervention and is used as a control condition.

During the measurement periods, participants received the MyMovez Wearable Lab: a smartphone with a tailor-made research application and a wrist-worn accelerometer. The accelerometer (Fitbit Flex) measured the physical activity per minute and per day, and was water-resistant. The smartphone was equipped with a custom made research application by which daily questionnaires were administered. Beginning in wave 5, the app contained a social platform in which the participants could communicate with each other. The smartphone also connected to the accompanying accelerometer and other research smartphones via Bluetooth.

Among others, the most important measures in the project are:

• Questionnaire data: e.g. Food Frequency Questionnaires, Self-reported media exposure, measures related to the theory of planned behavior
• Physical activity measured by accelerometer.
• Sociometric nominations: Peers nominated classmates on certain questions
• Proximity networks inferred from the Bluetooth connections on the research phones (beacon data)
• Online communication data derived from the social platform (Social Buzz)
• Photo data (not shared in this repository)
• BMI measured by the researchers

For more information please see the accompanying overview, or the protocol paper of the project: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5353-5

This data set contains all gather information of the MyMovez project, which investigated adolescents’ health behaviors (ie., nutrition, media use, and physical activity) and their social networks for three years. The first year (2016; data collection waves 1, 2, 3) and the second year (2017; wave 4) marked the first phase of the project in which the health behaviors of adolescents were monitored without intervening. The third year (waves 5, 6, 7) marked the second phase of the project in which four different types of interventions were tested to promote either water consumption or physical activity. A fifth group did not receive an intervention and is used as a control condition.During the measurement periods, participants received the MyMovez Wearable Lab: a smartphone with a tailor-made research application and a wrist-worn accelerometer. The accelerometer (Fitbit Flex) measured the physical activity per minute and per day, and was water-resistant. The smartphone was equipped with a custom made research application by which daily questionnaires were administered. Beginning in wave 5, the app contained a social platform in which the participants could communicate with each other. The smartphone also connected to the accompanying accelerometer and other research smartphones via Bluetooth.Among others, the most important measures in the project are:- Questionnaire data: e.g. Food Frequency Questionnaires, Self-reported media exposure, measures related to the theory of planned behavior- Physical activity measured by accelerometer.- Sociometric nominations: Peers nominated classmates on certain questions- Proximity networks inferred from the Bluetooth connections on the research phones (beacon data)- Online communication data derived from the social platform (Social Buzz)- Photo data (not shared in this repository)- BMI measured by the researchersFor more information please see the accompanying overview, or the protocol paper of the project: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5353-5 Dataset containing health behaviors, media use, and social network data of 1500 adolescents in the Netherlands.

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.

This dataset provides a critical nexus between social media engagement and the financial dimension of European interest groups' representation costs. Designed to facilitate multiple linear regression analysis, this dataset is a valuable tool for researchers, statisticians, and analysts seeking to unravel the intricate relationships between digital engagement and the financial commitments of these interest groups. The dataset offers a robust collection of data points that enables the exploration of potential correlations, dependencies, and predictive insights. By delving into the varying levels of social media presence across different platforms and their potential influence on the cost of representation, researchers can gain a deeper understanding of the interplay between virtual engagement and real-world financial investment. Accessible to the academic and research community, this dataset holds the promise of shedding light on the dynamic and evolving landscape of interest groups' communication strategies and their financial implications. With the potential to inform policy decisions and strategic planning, this dataset represents a stepping stone toward a more comprehensive understanding of the intricate web of relationships that shape the world of European interest groups. The variables included: 1. Twitter_link (Dummy): 1.1. Twitter_YES 1.2. Twitter_NO 2. Facebook_link (Dummy): 2.1. Facebook_YES 2.2. Facebook_NO 3. Instagram_link (Dummy): 3.1. Instagram_YES 3.2. Instagram_NO 4. Linkedin_link (Dummy): 4.1. Linkedin_YES 4.2. Linkedin_NO 5. Youtube_link (Dummy): 5.1. Youtube_YES 5.2. Youtube_NO 6. mean_cost (continus): The mean of the range of estimated cost of representation.

Here are a few use cases for this project:

1. Fashion Retail Assist: This computer vision model can enable fashion retailers to analyze people's clothing in real time to identify trends and customer preferences, aiding in tailoring their stock and marketing strategies.

2. Automated Personal Stylist: Integrated into a mobile app, "Dresser" could analyze individual's existing wardrobe items and suggest outfit combinations, stepping in as a personal stylist.

3. Public Safety Applications: The model can be used by security or law enforcement agencies to detect and identify individuals based on their clothing or accessories, assisting in cases such as missing persons or criminal investigation.

4. Fashion Education: It can be used in fashion education and training to help students learn about different clothing types, fashion trends, and style identification.

5. Social Media Marketing: Marketing teams can use the model to analyze clothing trends and preferences from social media platforms to create more targeted advertising campaigns.

Please cite the following paper when using this dataset:

N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract

The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization is considering whether the outbreak should be assessed as a “potential public health emergency of international concern” or PHEIC, as was done for the COVID-19 and Ebola outbreaks in the past. During this time, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset consists of a total of 157,172 tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7^th May 2022 to 13th July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files.

• Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022 to May 21, 2022)
• Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022 to May 27, 2022)
• Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022 to June 5, 2022)
• Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022 to June 11, 2022)
• Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the associated Tweet IDs: June 12, 2022 to June 30, 2022)
• Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 41520, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

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.