Update README.md

README.md

@@ -36,7 +36,7 @@ A comprehensive dataset of trending hashtags on Twitter/X from 2020 to 2025, con
 
 This dataset captures trending hashtags from Twitter/X (formerly Twitter) by analyzing Wayback Machine snapshots of trends24.in, providing insights into breaking news, viral content, cultural moments, and global events from 2020 to 2025.
 
-**Data Source**: Wayback Machine snapshots of x.com trending data via trends24.in
+**Data Source**: Wayback Machine snapshots of x.com trending data. 
 
 ### Dataset Structure
 
@@ -106,28 +106,6 @@ Trump,2025,2025-01-08,40000000,3
 - **Middle East**: Israel (3.9M in 2023), Gaza (26M in 2025), Iran (33M in 2025)
 - **Tragedies**: Kobe Bryant (9.4M), Charlie Kirk assassination (38M in 2025)
 
-**Sports Events**:
-- **Super Bowl**: Consistently trending (2024, 2022)
-- **World Cup**: Messi (3.3M in 2022), Argentina (2.8M in 2022)
-- **Olympics**: Tokyo2020 (3.2M in 2021)
-
-**Entertainment & Awards**:
-- **Met Gala**: 3.6M (2024), 3.0M (2023), 2.6M (2022)
-- **VMAs**: 3.8M (2023), 3.4M (2022)
-- **GRAMMYs**: 3.3M (2022), 3.0M (2021)
-- **Oscars**: Consistently trending annually
-
-**Holidays & Celebrations**:
-- **Christmas**: 3.7M (2022), 3.8M (2023), 3.2M (2020)
-- **New Year**: 54M (2025), 2.2M (2022), 1.9M (2021)
-- **Halloween**: 5.5M (2020), 3.6M (2022), 2.8M (2023)
-- **Thanksgiving**: 4.1M (2021), 3.1M (2022), 34M (2025)
-
-**K-Pop & Fan Culture**:
-- **BTS**: BTS BTS BTS (5.4M in 2021), BTSxAMAs (4.4M), BBMAsTopSocial (4.4M)
-- **BLACKPINK**: JISOO, multiple trending moments
-- **Fan birthdays**: Massive engagement for celebrity birthdays globally
-
 **Social Movements**:
 - **Thailand Protests**: Multiple hashtags with 3M+ tweets (2020-2021)
 - **SARSMUSTEND**: 3.1M tweets (Nigeria police brutality protests, 2020)
@@ -137,85 +115,6 @@ Trump,2025,2025-01-08,40000000,3
 - **WhatsApp**: 3.4M (2021) - Service outage
 - **Threads**: 2.7M (2023) - Meta's Twitter competitor launch
 
-### Tweet Volume Distribution
-
-- **50M+ tweets**: 1 trend (Kanye 2025 controversy)
-- **10M-50M tweets**: 5 trends (major breaking news)
-- **5M-10M tweets**: 8 trends (massive viral events)
-- **3M-5M tweets**: 50+ trends (highly viral moments)
-- **1M-3M tweets**: 200+ trends (viral trends)
-- **100K-1M tweets**: 2,000+ trends (popular topics)
-- **Under 100K**: 9,000+ trends (niche or emerging trends)
-
-### Geographic & Linguistic Diversity
-
-**Languages Represented**:
-- **English**: Majority of trends (US, UK, global)
-- **Japanese**: エイプリルフール (34M), バレンタイン (28M), ハロウィン (3.4M)
-- **Thai**: เยาวชนปลดแอก (8.0M), ม็อบ trends (3M+)
-- **Spanish**: Latin American trends, Spanish trends
-- **Arabic**: قاسم_سليماني, Middle East topics
-- **Korean**: Multiple K-pop and Korean cultural trends
-- **Portuguese**: Brazilian trends (Lula, political hashtags)
-
-**Regional Highlights**:
-- **United States**: Dominates with political, entertainment, and sports trends
-- **Thailand**: Strong protest and political movement representation
-- **India**: Celebrity birthdays, political trends
-- **Brazil**: Political and cultural trends
-- **South Korea**: K-pop dominance
-- **Middle East**: Geopolitical events
-
-## 📈 Use Cases
-
-This dataset is valuable for:
-
-- **Trend Analysis**: Understanding what drives viral content on Twitter/X
-- **Event Detection**: Identifying major news events and cultural moments
-- **Sentiment Analysis**: Analyzing public reaction to events
-- **Political Science**: Studying political discourse and election trends
-- **Cultural Studies**: Understanding global cultural phenomena
-- **Media Research**: Analyzing news cycles and information spread
-- **Crisis Communication**: Understanding how crises trend on social media
-- **Marketing Research**: Identifying viral patterns and engagement strategies
-- **Time Series Analysis**: Predicting trending patterns
-- **Natural Language Processing**: Multilingual trend analysis
-
-## 🔧 Usage
-
-### Loading the Dataset
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("ronantakizawa/twitter-trending-hashtags")
-```
-
-### Example Analysis
-
-```python
-import pandas as pd
-
-# Load the data
-df = pd.read_csv("twitter-trending-hashtags.csv")
-
-# Get top 10 trends of all time
-top_trends = df.nlargest(10, 'tweets')
-
-# Trends by year
-trends_2024 = df[df['year'] == 2024]
-
-# Filter by date range
-import pandas as pd
-df['peak_date'] = pd.to_datetime(df['peak_date'])
-jan_2025 = df[(df['peak_date'] >= '2025-01-01') & (df['peak_date'] < '2025-02-01')]
-```
-
-## ⚠️ Data Collection & Limitations
-
-### Data Source
-This dataset was collected from Wayback Machine snapshots of trends24.in, which aggregates Twitter/X trending data. The data represents peak trending moments captured in these historical snapshots.
-
 ### Important Limitations
 
 1. **Sampling Bias**: Data is based on available Wayback Machine snapshots, not continuous monitoring
@@ -226,45 +125,8 @@ This dataset was collected from Wayback Machine snapshots of trends24.in, which
 3. **Tweet Count Accuracy**: Numbers represent peak values from snapshots, not cumulative totals
 4. **Coverage Gaps**: Not all trending topics may be captured due to snapshot availability
 5. **Incomplete 2025**: 2025 data is incomplete as the year is ongoing
-6. **Regional Bias**: Trends may be biased toward certain regions based on trends24.in methodology
-
-### Data Quality Notes
-
-- 14 of 15 verified major trends (93.3% accuracy) match real-world events
-- Date discrepancies are typically ±1 day and explainable
-- Tweet volumes accurately reflect relative event importance
-- Rankings within years are based on peak tweet counts from snapshots
 
 ## 📝 Citation
 
 If you use this dataset, please cite:
 
-```bibtex
-@dataset{twitter_trending_hashtags_2025,
-  title={Twitter/X Trending Hashtags Dataset (2020-2025)},
-  author={Ronan Takizawa},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/ronantakizawa/twitter-trending-hashtags}
-}
-```
-
-## 📄 License
-
-This dataset is released under the MIT License.
-
-## 🙏 Acknowledgments
-
-- **Data Source**: Wayback Machine (archive.org) for preserving historical web snapshots
-- **Original Platform**: trends24.in for aggregating Twitter/X trending data
-- **Platform**: Twitter/X for the underlying social media platform
-
-## 📧 Contact
-
-For questions, issues, or suggestions:
-- **GitHub**: [Report an issue](https://github.com/ronantakizawa/datasets)
-- **Hugging Face**: [@ronantakizawa](https://huggingface.co/ronantakizawa)
-
----
-
-*Dataset compiled and published in November 2025*