Explore the fascinating world of TikTok with our comprehensive TikTok User Profiles Dataset. Whether you're a marketer, researcher, or enthusiast, this dataset provides a wealth of information on public TikTok profiles, allowing you to extract valuable business and non-business insights. You have the flexibility to purchase the complete dataset or tailor it to your specific needs by utilizing a range of filtering options.

Key Data Points:

• Timestamp
• Account name
• Nickname
• Bio
• Average engagement score
• Creation date
• Verification status
• Likes count
• Followers count
• External link in bio
• and many more

Popular Use Cases: Unleash the potential of this dataset for a variety of applications, including:

Sentiment Analysis: Gain deep insights into user sentiment by analyzing profiles' content, engagement, and interactions. Brand Monitoring: Track mentions of your brand, products, or services across TikTok, understanding how users perceive and engage with your offerings. Influencer Marketing: Identify potential influencers by assessing their follower count, engagement, and overall impact, helping you make informed collaboration decisions. Audience Insights: Understand your target audience by examining user bios, locations, and other profile details, aiding in tailoring your content and strategies.

Source: BrightData

Important : its a 3 month gap data Starting from March 2022 to Dec 2022

Influencers are categorized by the number of followers they have on social media. They include celebrities with large followings to niche content creators with a loyal following on social-media platforms such as YouTube, Instagram, Facebook, and Twitter.Their followers range in number from hundreds of millions to 1,000. Influencers may be categorized in tiers (mega-, macro-, micro-, and nano-influencers), based on their number of followers.

Businesses pursue people who aim to lessen their consumption of advertisements, and are willing to pay their influencers more. Targeting influencers is seen as increasing marketing's reach, counteracting a growing tendency by prospective customers to ignore marketing.

Marketing researchers Kapitan and Silvera find that influencer selection extends into product personality. This product and benefit matching is key. For a shampoo, it should use an influencer with good hair. Likewise, a flashy product may use bold colors to convey its brand. If an influencer is not flashy, they will clash with the brand. Matching an influencer with the product's purpose and mood is important.

https://sceptermarketing.com/wp-content/uploads/2019/02/social-media-influencers-2l4ues9.png">

Use our TikTok profiles dataset to extract business and non-business information from complete public profiles and filter by account name, followers, create date, or engagement score. You may purchase the entire dataset or a customized subset depending on your needs. Popular use cases include sentiment analysis, brand monitoring, influencer marketing, and more. The TikTok dataset includes all major data points: timestamp, account name, nickname, bio,average engagement score, creation date, is_verified,l ikes, followers, external link in bio, and more. Get your TikTok dataset today!

Aggregate statistics over 3,485,257 established TikTok creators: the follower-tier pyramid (median ~29,000 followers), niche mix, verification rate (2.4%), and public business-contact rate by tier — for creator discovery and influencer-marketing research.

Context

Find the top TikTok accounts.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

Data source: https://hypeauditor.com/top-tiktok/

Please upvote if you like this dataset

TikTok, known in China as Douyin (Chinese: 抖音; pinyin: Dǒuyīn), is a short-form video hosting service owned by Chinese company ByteDance. It hosts a variety of short-form user videos, from genres like pranks, stunts, tricks, jokes, dance, and entertainment with durations from 15 seconds to ten minutes. TikTok is an international version of Douyin, which was originally released in the Chinese market in September 2016. TikTok was launched in 2017 for iOS and Android in most markets outside of mainland China; however, it became available worldwide only after merging with another Chinese social media service, Musical.ly, on 2 August 2018.

TikTok and Douyin have almost the same user interface but no access to each other's content. Their servers are each based in the market where the respective app is available. The two products are similar, but features are not identical. Douyin includes an in-video search feature that can search by people's faces for more videos of them and other features such as buying, booking hotels and making geo-tagged reviews. Since its launch in 2016, TikTok and Douyin rapidly gained popularity in virtually all parts of the world. TikTok surpassed 2 billion mobile downloads worldwide in October 2020.

In this dataset you will find the details about top 1000 tiktokers all over the world.

Dataset collected for the study "Between Influence and Disinformation: Personal Branding and Responsible Communication of Influencers on TikTok." It contains coded data from 241 TikTok videos published by ten leading Spanish influencers between September and December 2025. Variables include engagement metrics, thematic classification, narrative tone, disinformation indicators, and commercial transparency markers.

This dataset documents the empirical material used in a study on the activity of lay Catholic influencers on TikTok in Poland. The temporal scope covers one full liturgical year in the Catholic Church: from December 1, 2024 to November 23, 2025.
The dataset includes 10 profiles and 1,790 video materials (TikToks) published within the analyzed period. The data were collected using desk research and are based exclusively on publicly available content. The sampling procedure was two-stage: (1) identification of content using a set of hashtags related to Catholic religion, (2) selection of profiles based on the number of followers, quality and creativity of publications, and recognition within religiously engaged communities.
The dataset does not include video files. This is due to the large volume of the material and restrictions related to further redistribution of content published on TikTok. Instead, it provides data that allow for clear identification of the sources (profiles with links), enabling access to the analyzed materials in their original publication environment.
The dataset has a documentary and methodological character. It enables verification of the empirical basis of the study and reconstruction of the sampling procedure.

Structured dataset of 30M+ creator profiles across YouTube, Instagram and TikTok, with up to 245 data points per creator including real engagement rate (computed against active followers), audience demographics (country, age, gender), sponsorship history with cost-per-video estimates, content-niche classification, verified contact emails and up to 4+ years of follower and engagement time-series. Refreshed almost daily.

Dataset The Role of Digital User Experience in TikTok Influencer Content on Generation Z Purchase Intention in Indonesia

The dataset consists of primary data collected through an online questionnaire distributed to followers of a selected TikTok influencer in Indonesia. Respondents were selected using purposive sampling based on predefined criteria. The data were measured using a Likert scale and include variables of influencer credibility, purchase intention, and purchase decision. The dataset was analyzed using SEM-PLS to examine the relationships between variables.

The TikTok Creator Profiles Dataset provides access to millions of publicly available TikTok creator profiles across industries, audience sizes, and regions worldwide.

Designed for marketers, agencies, researchers, and analytics teams, the dataset supports influencer discovery, market research, competitive analysis, audience intelligence, and creator economy insights.

Each profile may include publicly available information such as username, display name, bio, profile URL, follower count, following count, total likes, video count, verified status, category, country, language, external links, and contact details where available.

The dataset covers creators across major categories including lifestyle, beauty, fashion, gaming, fitness, technology, entertainment, travel, food, and more.

Data is available in CSV, JSON, and API formats, with regular updates to ensure fresh and reliable coverage of the global TikTok creator ecosystem.

Full edition for scientific use. The present dataset documents the use of the platform TikTok by Austrian politicians and political parties at the federal, state, and European levels. It includes all active TikTok accounts of members of state governments, the National Council, and the European Parliament, as well as those of state and federal political parties. For each account, the dataset provides reach and activity metrics. In addition, the ten most successful videos per account were categorized using a standardized content analysis conducted by a trained coding team. The dataset offers a systematic overview of reach, thematic emphases, and communication patterns of political actors on TikTok and provides an empirical basis for further research on political communication on visually oriented social media platforms.

This dataset contains 1,351 TikTok videos collected on April 8, 2026, via Apify TikTok Scraper. The data were gathered as part of a study examining the mediatization of Islamic piety within Indonesia's hijrah youth movement, with a focus on how religious authority is produced and contested across elite influencers, movement platforms, and local communities.

The dataset covers fourteen hashtag categories representing different levels of the Indonesian Islamic digital ecosystem: #hananattaki, #felixsiauw, #palestina, #dakwahtiktok, #pemudahijrah, #yukhijrah, #kajianislam, #viralhijrah, #temanhijrah, #hijrahtrend, #hijrahfest, #yukngaji, #khilafah, and #iran. For each video, the dataset includes engagement metrics (play count, like count, comment count, share count, save count), author metadata (username, follower count, verification status), video metadata (duration, format, language), caption text, hashtag co-occurrence data, and post timestamp.

The dataset was used to map visibility patterns across three levels of actors: elite digital influencers (macro), movement and community platforms (meso), and locally embedded communities (micro). It supports analyses of platform-mediated religious authority, algorithmic amplification of Islamic content, and transnational dimensions of hijrah discourse in Southeast Asia.

This is a dataset accompanying the paper “The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok” presented at the FAccT 2026 conference, designed to analyze video interactions, ad classifications, and user engagement patterns. It contains records of video interactions, including metadata about the videos, user demographics, and ad classifications, allowing the full replication of results presented in the paper.

The video excerpts included in this dataset are used solely as units of content for analytical purposes. They do not represent, reflect, or imply the personal views, intentions, or stance of the individuals who created them. Content should be interpreted as data artifacts, not as statements attributable to any person.

To minimize the risk of third-party misuse, the dataset is available only to researchers for non-commercial research purposes upon verification of their email address associated with academic organisation.

Paper: https://dl.acm.org/doi/10.1145/3805689.3812355

Preprint: https://arxiv.org/abs/2603.05653

GitHub repository: https://github.com/kinit-sk/ai-auditology-advertising-and-minor-profiling-tiktok

Acknowledgemet: This work was partially funded by the EU NextGenerationEU through the Recovery and Resilience Plan forSlovakia under the project AI-Auditology, No. 09I03-03-V03-00020.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following paper:

@inproceedings{10.1145/3805689.3812355,
  author = {Solarova, Sara and Mosnar, Matej and Tibensky, Matus and Jakubcik, Jan and Bindas, Adrian and Liska, Simon and Hossner, Filip and Mesar\v{c}\'{\i}k, Mat\'{u}\v{s} and Srba, Ivan},
  title = {The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok},
  year = {2026},
  isbn = {9798400725968},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3805689.3812355},
  doi = {10.1145/3805689.3812355},
  abstract = {Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unable to recognize or resist commercial persuasion. Article 28(2) of the Digital Service Act (DSA) responds to this vulnerability by prohibiting profiling-based advertising to minors. However, the regulation's narrow definition of “advertisement” excludes current advertising practices including influencer paid partnerships and brand promotional content that serve functionally equivalent commercial purposes. We provide the first empirical evidence of how this definitional gap operates in practice through an algorithmic audit of TikTok. Our approach deploys sock-puppet accounts simulating a pair of minor and adult users with matching interest profiles. The content recommended to these users is automatically annotated, enabling systematic statistical analysis across four video categories: containing formal, disclosed, undisclosed advertisement and non-advertisement; as well as advertisement topical relevance to user's interest. Our findings reveal a stark regulatory paradox. TikTok demonstrates formal compliance with Article 28(2) by shielding minors from profiled formal advertisements, yet both disclosed and undisclosed ads exhibit significant profiling aligned with user interests (5-8 times stronger than for adult formal advertising). The strongest profiling emerges within undisclosed commercial content, where creators/brands fail to label paid partnership/promotional content and the platform neither corrects this omission nor prevents its personalized delivery to minors. These results demonstrate that minors remain exposed to algorithmically targeted commercial content through the same recommendation mechanisms the DSA seeks to constrain. We argue that protecting minors requires expanding the definition of advertisement in EU law to encompass influencer and brand promotional content, and ensuring that any such expansion is accompanied by a corresponding prohibition on profiling-based targeting of minors, so that commercial content cannot circumvent protections merely by operating outside formal advertising channels.},
  booktitle = {Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency},
  pages = {4811–4835},
  numpages = {25},
  keywords = {Digital Services Act, advertisement, algorithmic auditing, minor profiling, TikTok},
  location = {},
  series = {FAccT '26}
}

Dataset Description

The logs of video presented to individual simulated users are provided in the ai-auditology-advertising-and-minor-profiling-tiktok_video_data.csv file. It is structured into 31 columns, capturing details such as session and video identifiers, timestamps, ad classifications, visual indicators, user demographics, and video metadata.