Update README.md

README.md

@@ -5,44 +5,114 @@ pretty_name: Farcaster Casts
 
 # Farcaster Public Casts Dataset
 
-This dataset contains 13,166,620 public casts (posts) from the Farcaster social protocol, spanning from October 13, 2023 to February 14, 2025.
+This dataset contains 157,054,986 public casts (posts) from the Farcaster social protocol that have not been deleted by their authors. The dataset includes comprehensive metadata for each cast, allowing for detailed analysis of the Farcaster ecosystem.
 
 ## Dataset Description
 
 The dataset contains the following fields for each cast:
-- `timestamp`: When the cast was created
-- `fid`: The Farcaster ID of the user who created the cast
-- `hash`: The unique hash identifier of the cast
-- `text`: The text content of the cast
+- `Fid`: The Farcaster ID of the user who created the cast
+- `MessageType`: Type of message (all are 'CastAdd' as deleted casts are excluded)
+- `Timestamp`: Seconds since the Farcaster Epoch (January 1, 2021 00:00:00 UTC)
+- `Hash`: The unique hash identifier of the cast
+- `SignatureScheme`: The cryptographic signature scheme used
+- `Signature`: Cryptographic signature of the cast
+- `Signer`: The public key of the signer
+- `Text`: The text content of the cast
+- `Mentions`: User mentions included in the cast
+- `ParentCastId`: ID of the parent cast if this is a reply
+- `ParentUrl`: URL of external content if this cast is a reply to external content
+- `Embeds`: JSON object containing any embedded content (URLs, images, etc.)
+
+## Timestamp Format
+
+Timestamps in this dataset use the Farcaster epoch (seconds since January 1, 2021 00:00:00 UTC).
+
+To convert to a standard Unix timestamp:
+1. Add 1609459200 (Unix timestamp for January 1, 2021 00:00:00 UTC) 
+2. The result will be a standard Unix timestamp
+
+Example in Python:
+```python
+# Convert Farcaster timestamp to Unix timestamp
+unix_timestamp = farcaster_timestamp + 1609459200
+
+# Convert to datetime
+from datetime import datetime
+dt = datetime.fromtimestamp(unix_timestamp)
+```
 
 ## Key Statistics
-- Total number of casts: 13,166,620
-- Date range: 2023-10-13 15:41:13 UTC to 2025-02-14 05:33:15 UTC
+- Total number of casts: 157,054,986
 - Format: Single Parquet file
+- Dataset includes all non-deleted casts from the Farcaster network as of February 28, 2025
 
 ## Intended Uses
+
 This dataset can be useful for:
-- Analyzing Farcaster conversations and trends
+- Analyzing conversation patterns and social interactions on Farcaster
+- Studying reply chains and community formation through mentions and replies
+- Tracking the evolution of discussions and trends
 - Training language models on social media discourse
-- Studying the growth and evolution of the Farcaster network
-- Research on decentralized social protocols
+- Analyzing embedded content sharing patterns
+- Researching decentralized social network growth and user behavior
+- Creating classifiers of likelihood of a cast going viral
+- Building network graphs of Farcaster users based on interactions
+
+## Example Queries
+
+Using Python and pandas:
+```python
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+# Load the dataset
+df = pd.read_parquet('casts.parquet')
+
+# Convert timestamps to datetime
+df['datetime'] = df['Timestamp'].astype(int).apply(
+    lambda x: datetime.fromtimestamp(x + 1609459200)
+)
+
+# Count casts per month
+monthly_counts = df.groupby(df['datetime'].dt.strftime('%Y-%m')).size()
+
+# Find most active users
+top_users = df.groupby('Fid').size().sort_values(ascending=False).head(10)
+
+# Analyze reply chains
+reply_percentage = (df['ParentCastId'].notna().sum() / len(df)) * 100
+```
+
+Using DuckDB:
+```sql
+-- Count casts by day
+SELECT 
+  DATE_TRUNC('day', TIMESTAMP '2021-01-01 00:00:00' + (CAST(Timestamp AS BIGINT) * INTERVAL '1 second')) AS cast_date,
+  COUNT(*) AS daily_cast_count
+FROM 'casts.parquet'
+GROUP BY cast_date
+ORDER BY cast_date;
+
+-- Find casts with external links
+SELECT * FROM 'casts.parquet'
+WHERE Embeds LIKE '%url%'
+LIMIT 100;
+```
 
 ## Limitations
-- Contains only public casts
 - Does not include user profile information
-- Does not include reactions, recasts, or replies structure
-- Limited to text content (no images or other media)
+- Does not include reactions or other engagement metrics
 
 ## Ethics & Privacy
 - This dataset contains only public casts that were publicly accessible on the Farcaster protocol
+- Deleted casts have been excluded to respect user content choices
 - Personal information beyond public FIDs has been excluded
 - Users should respect Farcaster's terms of service when using this data
-
-## Citation & Attribution
-If you use this dataset in your research, please provide attribution to the Farcaster protocol and community.
+- Researchers should be mindful of potential biases in social media datasets
 
 ## License
 MIT License
 
 ## Updates
-This is a static snapshot of Farcaster casts. For live data, please refer to the Farcaster protocol directly.
+This dataset represents a snapshot of Farcaster casts as of February 28, 2025. For live data, please refer to the Farcaster protocol directly or check for updated versions of this dataset.