TikTok Trending Hashtags (2022-2025)

A comprehensive dataset of trending hashtags on TikTok from 2022 to 2025, containing 1,830 unique hashtag entries across multiple years, languages, and cultural contexts.

📊 Dataset Description

This dataset captures trending hashtags from TikTok's Creative Center, providing insights into viral content, cultural moments, and global events from 2022 to 2025.

Data Source: TikTok Creative Center - Popular Hashtags

Dataset Structure

tag,year,rank,posts
2024,2025,1,3000000
2025,2025,2,2000000
valentinesday,2025,3,1000000
...

Columns: - tag (string): The hashtag name without the # symbol - year (integer): The year the hashtag was trending (2022-2025) - rank (integer): Rank within that year based on post count (1 = highest) - posts (integer): Total number of posts using this hashtag

Dataset Statistics

• Total Entries: 1,830 hashtags
• Years Covered: 2022-2025
• Languages: 10+ (English, Spanish, Arabic, Thai, Vietnamese, Portuguese, Chinese, Russian, Korean, and more)
• Categories: Sports, Entertainment, News, Games, Cultural Events, Politics, Holidays

Breakdown by Year: - 2025: 586 hashtags (most recent data) - 2024: 909 hashtags (most comprehensive) - 2023: 329 hashtags - 2022: 6 hashtags (limited early data)

🔍 Key Insights

Top Trending Hashtags by Year

Year	#1 Hashtag	Posts	Theme
2025	#2024	3,000,000	Year-in-review
2024	#christmas	3,000,000	Holiday season
2023	#2024	2,000,000	New year anticipation
2022	#newyear	286,000	New year celebration

Trends

Hashtags appearing in multiple years (evergreen content): - #happynewyear - Present in 5 different contexts - #mondaymotivation - Consistent weekly trend across 5 instances - #benfica - Sports team trending across 5 periods - #newyear - 4 years of coverage - #valentinesday - Annual romantic holiday - #superbowl - Annual sports event

2024 Highlights: - Elections: #trump (267K), #election2024 (136K), #kamalaharris (97K) - Sports: #copaamerica (362K), #olympics (25K), #messi (489K) - Entertainment: #squidgame (1M), #deadpool (32K), #billieeilish (199K) - Holidays: #christmas (3M), #valentinesday (1M), #diademuertos (956K)

2023 Highlights: - Disney Centennial: #disney100 (829K) - Gaming: #fnaf (788K) - Cultural: #recuerdame (776K)

2022 Highlights: - Soccer Legend: #pele (117.7K) - Viral Trends: #facechange (69.2K)

Most Popular Categories: 1. Holidays & Celebrations (30%+): Christmas, New Year, Valentine's Day, Halloween 2. Sports & Outdoor (20%+): Soccer, NFL, Olympics, Basketball 3. Entertainment & News (25%+): Movies, TV shows, Celebrity news 4. Gaming (10%): Squid Game, FNAF, Fortnite, Mobile Legends 5. Cultural Events (10%): Dia de Muertos, Ramadan, Lunar New Year 6. Politics & Social (5%): Elections, protests, social movements

Post Count Distribution: - Million+ posts: 8 hashtags (mega-viral content) - 500K-1M posts: 15 hashtags (highly viral) - 100K-500K posts: 250+ hashtags (popular trends) - Under 100K: Majority (niche or emerging trends)

As of January 2022, the hashtag "fyp," which stands for "for you page," was the most used hashtag on TikTok, amassing over 18.57 trillion views across posts using it. The hashtag "viral" ranked second, with approximately 6.3 trillion views on TikTok short-video posts using the hashtag. Posts using the hashtag "duet," which refers to TikTok videos that can be shared, mirrored, and commented on by creators, collected around 2.4 trillion views as of January 2022.

🔥 What Makes Content Go Viral?

This dataset is designed to help data scientists, analysts, and researchers understand, analyze, and predict viral content across major social media platforms. It captures realistic engagement behavior, sentiment signals, and content attributes that influence virality in today’s digital ecosystem.

🌐 Platforms Covered

The dataset includes multi-platform data from: - TikTok - Instagram - X (Twitter) - YouTube Shorts

Each platform is represented with consistent metrics, making cross-platform comparison easy and reliable.

🧠 Dataset Features (Columns Explained)

🆔 Post Metadata

• post_id – Unique identifier for each post
• platform – Social media platform name
• content_type – Video, image, carousel, or text
• topic – Content category (Entertainment, Tech, Sports, etc.)
• language – Post language (EN, UR, HI, ES, FR)
• region – Geographic region of the post

⏰ Time & Trend Signals

• post_datetime – Date and time of posting Useful for time-series analysis, peak engagement detection, and trend forecasting.

#️⃣ Hashtags & Sentiment

• hashtags – Multiple trending hashtags per post
• sentiment_score – Emotional tone score (-1 = negative, +1 = positive)

Ideal for NLP tasks, sentiment analysis, and hashtag impact studies.

📈 Engagement Metrics

• views – Total views
• likes – Likes received
• comments – Number of comments
• shares – Number of shares

These metrics allow deep analysis of user interaction patterns.

⚙️ Engineered Features

• engagement_rate – Combined engagement normalized by views
• is_viral – Binary label indicating viral content

Perfect for machine learning models and classification tasks.

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 ...

The dataset was originally obtained from TikTok's trending API by a GitHub user named Ivan Tran. It contains metadata on engagement with user-created videos and user profile data. The original create time is in Unix timecode format and is extracted directly from the video id number. TikTok's API has become much more difficult to access recently, so more current data is harder to obtain. The hashtags column contains lists.

This dataset is an extension of the TikHarm dataset, created to enhance multimodal harmful content detection on TikTok. It was developed as part of the MTikGuard system, a real-time moderation pipeline designed to protect young audiences from unsafe TikTok videos.

🔹 Purpose

The dataset supplements TikHarm with 775 additional annotated videos, collected from TikTok trending and targeted hashtag queries. These videos were selected to address class imbalance and content diversity gaps in the original dataset, improving model generalization for real-world deployment.

🔹 Content

Each video is labeled into one of four categories: - Safe - Adult Content - Harmful Content (e.g., dangerous challenges, graphic violence) - Suicide / Self-harm

🔹 Data Collection & Annotation

Collection: Automated crawling using Selenium and TikTok Content Scraper, coordinated via Apache Airflow and Apache Kafka.

Annotation: Conducted via a custom web-based tool, following detailed guidelines to ensure consistency and reliability. Multiple annotators reviewed each video, with disagreements resolved via majority voting.

Class balance: Oversampling of underrepresented categories (e.g., Suicide, Harmful Content) during collection.

🔹 Applications

Training and evaluating multimodal classification models for harmful content detection.

Benchmarking real-time content moderation pipelines.

Research on multimodal fusion strategies and multi-label classification.

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

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The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

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Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

YouTube Shorts & TikTok Trends 2025

Overview

A global dataset capturing short-form video performance across YouTube Shorts and TikTok in 2025.
It includes over 50,000 video records, available in both raw and machine learning–ready formats.
Designed for reproducible EDA, dashboarding, and baseline ML modeling on social media engagement dynamics.

Files Included

File	Description	Shape
youtube_shorts_tiktok_trends_2025.csv	Raw video-level data with full feature set	~48k × ~58
youtube_shorts_tiktok_trends_2025_ml.csv	ML-ready, cleaned and engineered version	~50k × 32
monthly_trends_2025.csv	Monthly aggregates (Jan–Aug 2025)	~480 × 8
country_platform_summary_2025.csv	Country × platform summary statistics	~60 × 14
top_hashtags_2025.csv	Ranked list of top trending hashtags	~82 × 18
top_creators_impact_2025.csv	Creator-level impact and influence metrics	~1,000 × 20
DATA_DICTIONARY.csv	Column names and definitions	~58 × 2

All files are UTF-8 encoded, cleaned, and schema-aligned for direct analysis.

Key Columns (ML-Ready File)

• Identifiers: video_id, platform, country, category, creator_tier
• Engagement Metrics: views, likes, comments, shares, saves, completions
• Derived Ratios: engagement_rate = (likes + comments + shares) / views, plus save_rate, share_rate, comment_rate
• Signals: velocity indicators, rolling statistics, seasonality flags

Recommended Uses

• EDA: Analyze short-form engagement trends by country, platform, or content type
• ML Modeling: Classify trend_label or predict engagement_rate and views
• Dashboarding: Visualize global video trends and creator performance
• Market Research: Study cultural and regional patterns of viral content

Notes

• trend_label is a snapshot trend proxy; baseline models typically reach 25–35% accuracy without temporal features.
• publish_date_approx is derived and coarse — for trend direction only.
• The dataset contains metadata only (no media content).

If you find this dataset helpful, supporting it with an upvote helps others discover it too ✨

🎬 Movie Success Prediction Dataset

This dataset was curated to support machine learning models that predict movie success based on a wide range of multi-modal features, including cast popularity, sentiment analysis, audio-visual cues, social media engagement, and metadata such as budget and IMDb rating.

📦 Dataset Overview

The dataset consists of 36 engineered features extracted from various sources:

• Cast and Crew Insights (e.g., popularity trends, number of cast members)
• Sentiment Analysis from YouTube Comments using VADER
• Audio Features from movie trailers using VGGish 3
• Video Features using ResNet-based frame analysis
• TikTok Popularity Signals (hashtags, views, engagement rate)
• Movie Metadata (e.g., budget, IMDb rating)

Each row represents one movie. The dataset is ideal for classification or regression tasks related to box office success, revenue prediction, or audience engagement forecasting.

📊 Feature Mapping

Feature Code	Feature Name
Feature_1	cast_trend_1
Feature_2	cast_trend_2
Feature_3	cast_trend_3
Feature_4	avg_cast_popularity
Feature_5	top_cast_popularity
Feature_6	genre_score
Feature_7	positive_sentiment
Feature_8	neutral_sentiment
Feature_9	negative_sentiment
Feature_10	num_youtube_comments
Feature_11	num_cast_members
Feature_12	num_upcoming_movies
Feature_13	avg_upcoming_popularity
Feature_14	max_upcoming_popularity
Feature_15	tiktok_hashtag_views
Feature_16	tiktok_video_count
Feature_17	tiktok_total_likes
Feature_18	tiktok_total_comments
Feature_19	tiktok_total_shares
Feature_20	tiktok_engagement_rate
Feature_21	audio_tempo
Feature_22	audio_energy_mean
Feature_23	audio_energy_variance
Feature_24	audio_spectral_centroid_mean
Feature_25	audio_spectral_rolloff_mean
Feature_26	video_brightness_mean
Feature_27	video_colorfulness_mean
Feature_28	video_scene_change_rate
Feature_29	video_emotion_happy
Feature_30	video_emotion_sad
Feature_31	imdb_rating
Feature_32	budget
Feature_33	log_budget
Feature_34	sqrt_budget
Feature_35	budget_squared
Feature_36	budget_rating_interaction

🛠️ Feature Engineering Highlights

• Audio features were extracted using the VGGish 3 model, widely used in speech emotion recognition tasks.
• Video features were obtained from a ResNet-based model analyzing brightness, scene change rate, colorfulness, and emotion cues.
• Sentiment scores were derived from YouTube comments using VADER, capturing positive, neutral, and negative sentiment proportions.
• TikTok engagement metrics were collected using hashtag data, capturing likes, views, shares, and overall engagement rate.
• Budget transformations such as log, square root, and squared values are included, along with an interaction feature with IMDb rating.

💡 Potential Use-Cases

• Predict box office revenue or success labels
• Analyze which audio-visual cues correlate with public interest
• Build early-stage predictors of movie success using trailers and social signals
• Inform marketing strategies using real-time sentiment and TikTok trends

📥 Data Sources

• IMDb for metadata
• YouTube (comments and trailers) for sentiment and audio/visual analysis
• TikTok for hashtag popularity and engagement stats
• In-house processing for video/audio feature extraction using ResNet and VGGish 3

🚀 Whether you're working on predictive modeling, multimedia analysis, or social signal correlation, this dataset provides a diverse feature set to explore what makes a movie successful.