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Social Media Trending Data
Social Media and Posts
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About
This dataset offers an in-depth look into popular TikTok videos, authors, and music. It provides valuable insights into what drives content to trend on one of the world's leading social media platforms. With this data, users can analyse popular video types, understand user engagement metrics like likes, shares, and comments, and identify successful content strategies to develop their own viral content. The dataset includes details on videos, the users who posted them, and characteristics of popular authors and trending music.
Columns
The dataset is structured across several files, each containing specific columns:
File: tiktok_collected_liked_videos.csv
user_name: The name of the user who posted the video (String).user_id: The unique identifier for the user (Integer).video_id: The unique identifier for the video (Integer).video_desc: The description of the video (String).video_time: The timestamp of the video (Integer).video_length: The duration of the video (Integer).video_link: The URL link to 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
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
author_nickname: The author's nickname (String).author_avatarThumb: The author's avatar thumbnail (String).author_signature: The author's signature (String).author_verification: Indicates if the author's account is verified (Boolean).author_privateAccount: Indicates if 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
Author ID: The author's TikTok ID (String).Author Nickname: The author's TikTok nickname (String).Avatar Thumbnail: The author's TikTok avatar thumbnail (String).Signature: The author's TikTok signature (String).Verified?: Indicates if the author's TikTok account is verified (Boolean).Private Account?: Indicates if the author's TikTok account is private (Boolean).
File: trending_videos.csv
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).
Distribution
The data is provided in CSV file format. One of the included files,
tiktok_collected_liked_videos.csv, has a size of 23.65 kB. While specific row or record counts for all files are not explicitly stated, sample data for tiktok_collected_liked_videos.csv indicates observations for 100 records for various columns, with some columns showing value ranges suggesting a larger underlying dataset.Usage
This dataset is ideal for a variety of applications including:
- Identifying popular TikTok authors for further analysis.
- Finding trending videos on TikTok to understand virality mechanisms.
- Generating lists of videos tagged with specific hashtags, such as #funny.
- Analysing content performance and user engagement metrics.
- Informing content creation strategies for social media marketers and creators.
Coverage
The dataset covers a time range approximately from February to August 2021, based on the
video_time timestamps observed in the sample data. The geographic and demographic scope is related to TikTok's global user base, though specific regional or demographic breakdowns are not detailed within the dataset itself.License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
This dataset is valuable for a wide range of users, including:
- Social Media Analysts: To study trends, virality, and platform dynamics.
- Content Creators: To understand successful video characteristics and identify popular music or authors for collaborations.
- Marketers: To identify trending content and authors for campaign planning.
- Researchers: To conduct academic studies on social media behaviour and digital culture.
- Data Scientists: For building predictive models related to content virality or user engagement.
Dataset Name Suggestions
- TikTok Trend Insights
- TikTok Viral Content Data
- Social Media Trending Data
- TikTok Performance Analytics
- TikTok Creator & Content Data
Attributes
Original Data Source: Social Media Trending Data
QUALITY