TikTok is the leading destination for short-form mobile video. The platform is built to help imaginations thrive. TikTok's mission is to create a place for inclusive, joyful, and authentic content–where people can safely discover, create, and connect.

https://s3-prod.adage.com/s3fs-public/20230807_celeb_run_agencies_3x2.jpg" alt="Celebs"> The dataset you provided appears to focus on TikTok celebrities and contains the following columns:

Celebrity: The name or handle of the TikTok celebrity. Followers: The number of followers the celebrity has, often represented in millions or billions. Following: The number of accounts the celebrity follows, which may be represented as thousands (K) or just a number. Likes: The total number of likes the celebrity’s videos have received, often represented in millions or billions. T.Videos: The total number of videos posted by the celebrity. Video Duration: The typical duration of their videos, which ranges from a few seconds (e.g., 10 - 15 seconds) to over a minute. Average Views: The average number of views their videos receive, often in millions. Net Worth: The estimated net worth of the celebrity, often represented in millions or billions of dollars. Most Viewed Video: The number of views for their most popular video, usually in millions or billions. Most Liked Video: The number of likes for their most popular video, represented in millions or billions. Video Category: The types or categories of videos the celebrity posts, such as comedy, dance, acting, challenges, etc.

The reviews and ratings for the TikTok application on the Android platform provide valuable insights into user experiences, satisfaction levels, and overall performance of the app. TikTok, a popular social media platform known for its short-form video content, has garnered millions of downloads and active users worldwide. On the Google Play Store, users have the opportunity to rate the app on a scale of 1 to 5 stars and leave detailed reviews highlighting their thoughts, feedback, and suggestions.

Positive reviews often praise TikTok for its user-friendly interface, innovative video editing tools, and the ability to discover entertaining and creative content from a diverse global community. Many users appreciate the app's algorithm, which curates personalized content tailored to individual preferences, making it highly engaging and addictive. Additionally, the frequent updates and introduction of new features, such as filters, effects, and music integration, are frequently mentioned as reasons for high ratings.

On the other hand, some negative reviews highlight concerns about privacy, data security, and the presence of inappropriate content. A few users have reported occasional bugs, crashes, or performance issues, particularly on older Android devices. Despite these criticisms, TikTok's overall rating remains high, reflecting its widespread popularity and the enjoyment it brings to the majority of its users. The reviews and ratings collectively serve as a useful resource for potential new users to gauge the app's strengths and weaknesses before downloading it.

As of February 2025, it was found that around 14.1 percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately 16.6 percent of the platform's audience. The online audience of the popular social video platform was further composed of 14.6 percent of female users aged between 25 and 34 years, and 20.7 percent of male users in the same age group.

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.

Trending on TikTok

We are probably all familiar with TikTok. People tend to spend hours each day scrolling through the millions of videos which are uploaded every single day. Not to mention the uploaders who are giving anything to get as many likes and followers as possible. But what makes one TikTok video a true hit or a miss? I give you an opportunity to figure this out ;)

I scraped the first 1000 trending videos on TikTok, using an unofficial TikTok web-scraper. Note to mention I had to provide my user information to scrape the trending information, so trending might be a personalized page. But that doesn't change the fact that certain people and videos got a certain amount of likes and comments.

I transformed the data into usable csv files and attached the actual videos as well.

What's in the files

Videos.zip This file contains the actual 1000 trending TikTok videos. Each filename corresponds to the id key in the trending.json file.

trending.json The raw scraped dataset. I figured splitting up the dataset resulted in messy errors. For example: a user might have one avatar while posting a video and another while posting the next video. This resulted in multiple users with the same name, id etc. except for the avatar. So I decided to post the raw data and I will show you how to translate this multi-level JSON structure to a single DataFrame in my first Notebook.

Acknowledgements

Many thanks to Andrew Nord the creator of the tiktok-scraper, and his contributers.

Inspiration

So what does make a TikTok video a true hit? Is it the moment when a video is uploaded? Or perhaps the amount of followers is an important factor? Maybe the hashtags or even the music being used?

So... are you the one who unlocks the mystery?

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

###### Title: Top 20 Countries with the Largest TikTok User Base (2024)

### Subtitle: An Insight into TikTok's Global Reach and Popularity

Description: This dataset provides the top 20 countries with the highest number of TikTok users as of 2024. TikTok, as one of the most widely used social media platforms globally, continues to grow in popularity, attracting users from diverse demographics and regions. Understanding where the majority of TikTok users reside can offer valuable insights for marketers, businesses, and researchers interested in global digital trends. Dataset Information:

Source: Compiled from various statistical reports including (RouteNote: Digital Music Distribution
)ps://​(Business of Apps
)ntries-by-tiktok-users/) and Business of Apps.
Time Period: 2024
Number of Records: 20 countries
Global User Base: Over 1.5 billion monthly active users worldwide

Columns Description:

Country: The name of the country.
Users: The estimated number of TikTok users in that country (in millions).
Percentage of Global Users: The percentage share of global TikTok users that each country represents.
Year: The year the data was collected (2024).

Key Insights:

The United States leads with over 150 million users, followed by Indonesia with 126 million, and Brazil with 99 million.
Countries from diverse regions such as Asia (Indonesia, Vietnam, the Philippines), Latin America (Brazil, Mexico), and Europe (France, Germany) feature prominently in this list.
TikTok has shown exceptional growth in emerging markets like Southeast Asia, reflecting the app's global appeal beyond just the Western world.

Potential Uses:

Market Analysis: This dataset can be used to analyze TikTok's global distribution, helping brands and content creators to target specific regions.
Social Media Research: Researchers can study trends in TikTok adoption and usage across different regions.
Business Strategy: This dataset is ideal for companies looking to expand their digital marketing strategies or launch influencer campaigns in regions with the most active users.

Tags:

TikTok, Social Media, Global Users, Digital Trends, Social Media Marketing

During the fourth quarter 2024, approximately 20.6 million TikTok accounts were removed from the platform due to suspicion of being operated by users under the age of 13. During the last measured period, around 185 million fake accounts were removed from fake accounts removed from TikTok.

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.

The dataset includes engagement metrics such as the number of plays, likes, shares, and comments for all videos posted by news publishers on TikTok up to July 2023.

If you use this dataset in any publication or study, please cite: Cheng, Z., & Li, Y. (2023). Like, Comment, and Share on TikTok: Exploring the Effect of Sentiment and Second-Person View on the User Engagement with TikTok News Videos. Social Science Computer Review, 42(1), 201-223. https://doi.org/10.1177/08944393231178603

Popular TikTok Videos, Authors, and Musics

A Comprehensive Dataset for performing Trending Analysis

About this dataset

TikTok is one of the hottest social media platforms out there, and it's only getting bigger. If you're looking to get in on the action, this dataset is for you!

This dataset contains a collection of videos from TikTok, including information on the user who posted the video, the number of likes, shares, and comments the video received, as well as the video's length and description. With this data, you can see what types of videos are popular on TikTok and start planning your own viral content!

How to use the dataset

1. The dataset contains a collection of videos from the social media platform TikTok.
2. The videos include information on the user who posted the video, the number of likes, shares, and comments the video received, as well as the video's length and description.
3. The dataset also contains information on popular TikTok authors, including their unique ID, nickname, avatar thumbnail, signature, and whether or not their account is verified or private.
4. Additionally, the dataset includes a list of trending videos on TikTok, as well as the number of likes, shares, comments, and plays each video has received

Research Ideas

• Identifying popular TikTok authors to target for scraping videos and liked videos
• Finding trending videos on TikTok for further analysis
• Generating a list of videos from the TikTok app that are tagged with the #funny hashtag

Acknowledgements

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: tiktok_collected_liked_videos.csv | Column name | Description | |:---------------|:---------------------------------------------------------| | user_name | The name of the user who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of shares the video has received. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: tiktok_collected_videos.csv | Column name | Description | |:---------------|:---------------------------------------------------------| | user_name | The name of the user who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of shares the video has received. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: tiktok_funny_hashtag_videos.csv | Column name | Description | |:--------------------------|:-----------------------------------------------------------| | author_nickname | The author's nickname. (String) | | author_avatarThumb | The author's avatar thumbnail. (String) | | author_signature | The author's signature. (String) | | author_verification | Whether or not the author's account is verified. (Boolean) | | author_privateAccount | Whether or not the author's account is private. (Boolean) | | author_followingCount | The number of people the author is following. (Integer) | | author_followerCount | The number of people following the author. (Integer) | | author_heartCount | The number of hearts the author has. (Integer) | | author_diggCount | The number of diggs the author has. (Integer) | | music_title | The title of the music. (String) | | music_playUrl | The play url of the music. (String) | | music_coverThumb | The cover thumbnail of the music. (String) | | music_authorName | The author name of the music. (String) | | music_originality | The originality of the music. (String) | | music_duration | The duration of the music. (String) |

File: trending_authors.csv | Column name | Description ...

Don't forget to upvote, comment, and follow if you are using this dataset. If you have any questions about the dataset I uploaded, feel free to leave them in the comments. Thank you! :)

Jangan lupa untuk upvote, comment, follow jika anda menggunakan dataset ini, dan jika ada pertanyaan mengenai dataset yang saya upload, silahkan tinggalkan di comment. Terima kasih :)

Column Descriptions (English) 1. reviewId: A unique ID for each user review. 2. userName: The name of the user who submitted the review. 3. userImage: The URL of the user's profile picture. 4. content: The text content of the review provided by the user. 5. score: The review score given by the user, typically on a scale of 1-5. 6. thumbsUpCount: The number of likes (thumbs up) received by the review. 7. reviewCreatedVersion: The app version used by the user when creating the review (not always available). 8. at: The date and time when the review was submitted. 9. replyContent: The developer's response to the review (no data available in this column). 10. repliedAt: The date and time when the developer's response was submitted (no data available in this column). 11. appVersion: The app version used by the user when submitting the review (not always available).

Deskripsi Kolom (Bahasa Indonesia) 1. reviewId: ID unik untuk setiap ulasan yang diberikan pengguna. 2. userName: Nama pengguna yang memberikan ulasan. 3. userImage: URL gambar profil pengguna yang memberikan ulasan. 4. content: Isi teks ulasan yang diberikan oleh pengguna. 5. score: Skor ulasan yang diberikan pengguna, biasanya dalam skala 1-5. 6. thumbsUpCount: Jumlah suka (thumbs up) yang diterima oleh ulasan tersebut. 7. reviewCreatedVersion: Versi aplikasi yang digunakan pengguna saat membuat ulasan (tidak selalu tersedia). 8. at: Tanggal dan waktu saat ulasan dibuat. 9. replyContent: Isi balasan dari pengembang aplikasi terhadap ulasan (tidak ada data dalam kolom ini). 10. repliedAt: Tanggal dan waktu saat balasan dari pengembang diberikan (tidak ada data dalam kolom ini). 11. appVersion: Versi aplikasi yang digunakan pengguna saat memberikan ulasan (tidak selalu tersedia).

the Tik Tok app became popular in 2019 and during the pandemic it exploded in popularity and this made me inspired to create a profitability topic that Tik Tok is giving to sub-famous people to have monetary collection and have money to increase their content productivity and consequently improve your cinematography condition and your condition to have a better living condition for your monetization contribution and this inspired me to create about the thousand most profitable Tik tokers

In 2023, the number of TikTok users in Malaysia was estimated to reach around ** million. The number was forecast to continuously increase between 2024 and 2029. Based on the forecast, the number of TikTok users in Malaysia will reach **** million by 2029.User figures, shown here with regards to the platform TikTok, have been estimated by considering 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).

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, “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).

TikTok Discourse on Ukraine Invasion

6 Million User's 16K Videos, 12M Comments

By [source]

About this dataset

This dataset provides unprecedented insight into public opinion and discourse related to a major foreign policy event: the hypothetical invasion of Ukraine in 2022. Through this dataset, researchers have access to 16 thousand TikTok videos, spanning 6 million unique users, as well as 12 million associated comments. Explore discourse themes on the platform and investigate how opinions are shaped by political events through sentiment analysis. As further research develops, compare findings from this dataset with similar datasets from other social media platforms to better illuminate the nature of digital public opinion and its potential influence on national policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an opportunity to gain a broad understanding of how users engage with and contribute to the conversation around a major political event on the TikTok platform. Here are some tips on how you can use this dataset:

• Analyze User Engagement: You can study user engagement by exploring the comment threads associated with each video in the dataset, examining trends for particular user types or locations, or exploring any features that could have predictive value in terms of engagement levels.
• Compare User Participation: You can compare user participation from different countries or regions by analyzing comments and likes over time in relation to nationality. This would allow you to better understand where conversations about this particular event is most popular, and which countries/regions are more likely to have an opinion about it.
• Explore Topics & Narratives: By taking advantage of NLP techniques such as sentiment analysis and topic modeling on comments data, you will be able to uncover common themes amongst videos with shared narratives related the event in question

By leveraging these tools, you will be able to extract meaning from this massive dataset and gain insightful information into individual users’ behavior as well as overall discourse around the invasion of Ukraine in 2022

Research Ideas

• Cultural attitudes towards the invasion of Ukraine in 2022: This dataset can be used to determine public attitudes towards the event by analyzing both the comments and videos from users, providing an alternative means of studying cultural predispositions than traditional polls or surveys.
• Influence of online communities on discussing issues: This dataset can be used to study how online communities influence people’s mindset and opinions on a certain topic. By analyzing how conversations change across different platforms, academics may be able to determine what makes certain communities more effective at forming consensus around issues compared to others.
• Interpersonal dynamics among users regarding significant events: Analyzing this data can shed light into how conversations turn into heated debates between two groups of users, establishing either agreement or dissent over a particular topic matter related to the invasion in 2022 as well as identifying which individuals are influential among certain circles for sparking engagement with their ideas or statements about their views towards said event

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: video_ids.csv

Acknowledgements

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

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.

The broader adoption of social media platforms (e.g., TikTok), combined with recent developments in GAI technologies has had a transformative effect on many peoples’ ability to confidently to assess the veracity and meaning of information online. In this paper, building on recent related work that surfaced the social ways that young people evaluate information online, we explore the decision-making practices, challenges and heuristics involved in young adults’ assessments of information online. To do so, we designed and conducted a novel digital diary study, followed by data-informed interviews with young adults.