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README.md

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+# Bookmark Dataset
+
+A collection of 11783 bookmarks from various sources including Twitter, GitHub, and Raindrop.io.
+
+## Dataset Description
+
+This dataset contains bookmarks collected from various sources. Each record includes:
+
+- Basic bookmark information (ID, title, URL, content)
+- Source-specific metadata (Twitter metrics, GitHub repository info, etc.)
+- Temporal information (creation date, year, month)
+- Content analysis features (domain, content length)
+
+## Source Distribution
+
+```
+source
+raindrop        6147
+twitter         2612
+twitter_like    2105
+github           919
+```
+
+## Top Domains
+
+```
+domain
+twitter.com                  7625
+github.com                   1441
+x.com                         277
+arxiv.org                     158
+chat.openai.com                53
+huggingface.co                 47
+app.raindrop.io                33
+colab.research.google.com      26
+www.youtube.com                25
+www.semanticscholar.org        20
+```
+
+## Usage
+
+This dataset can be used for:
+
+- Analyzing bookmark patterns and trends
+- Understanding content consumption across different platforms
+- Training recommendation systems
+- Studying information organization
+
+## Data Format
+
+The dataset is provided in JSONL format with fields for common attributes and source-specific metadata.
+
+Generated on: 2025-03-28

analyze_bookmarks.py

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+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+import seaborn as sns
+
+# Set style
+plt.style.use('ggplot')
+sns.set(style="whitegrid")
+
+# Load the data
+data_path = Path("data.jsonl")
+bookmarks = []
+with open(data_path, 'r', encoding='utf-8') as f:
+    for line in f:
+        bookmarks.append(json.loads(line))
+
+# Convert to DataFrame
+df = pd.DataFrame(bookmarks)
+
+print(f"Loaded {len(df)} bookmarks")
+
+# Basic statistics
+print("\nSource Distribution:")
+source_counts = df['source'].value_counts()
+print(source_counts)
+
+print("\nTop Domains:")
+domain_counts = df['domain'].value_counts().head(20)
+print(domain_counts)
+
+# Create output directory for plots
+plots_dir = Path("plots")
+plots_dir.mkdir(exist_ok=True)
+
+# Source distribution pie chart
+plt.figure(figsize=(10, 7))
+source_counts.plot.pie(autopct='%1.1f%%', textprops={'fontsize': 10})
+plt.title('Bookmark Sources', fontsize=14)
+plt.ylabel('')
+plt.savefig(plots_dir / 'source_distribution.png', bbox_inches='tight')
+plt.close()
+print("Created source distribution chart: plots/source_distribution.png")
+
+# Top domains bar chart
+plt.figure(figsize=(12, 8))
+domain_counts.head(15).plot.barh()
+plt.title('Top 15 Domains', fontsize=14)
+plt.xlabel('Count')
+plt.ylabel('Domain')
+plt.tight_layout()
+plt.savefig(plots_dir / 'top_domains.png', bbox_inches='tight')
+plt.close()
+print("Created top domains chart: plots/top_domains.png")
+
+# Bookmarks by month (if date data is available)
+if 'created_at' in df.columns:
+    try:
+        # Convert to datetime if not already
+        if not pd.api.types.is_datetime64_any_dtype(df['created_at']):
+            df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce')
+        
+        # Extract year and month
+        df['year_month'] = df['created_at'].dt.to_period('M')
+        
+        # Count bookmarks by month
+        monthly_counts = df.groupby('year_month').size()
+        
+        plt.figure(figsize=(14, 8))
+        monthly_counts.plot.bar()
+        plt.title('Bookmarks by Month', fontsize=14)
+        plt.xlabel('Month')
+        plt.ylabel('Count')
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        plt.savefig(plots_dir / 'bookmarks_by_month.png', bbox_inches='tight')
+        plt.close()
+        print("Created bookmarks by month chart: plots/bookmarks_by_month.png")
+        
+        # Bookmarks by source over time
+        source_time = pd.crosstab(df['year_month'], df['source'])
+        
+        plt.figure(figsize=(14, 8))
+        source_time.plot.area(alpha=0.6)
+        plt.title('Bookmarks by Source Over Time', fontsize=14)
+        plt.xlabel('Month')
+        plt.ylabel('Count')
+        plt.legend(title='Source')
+        plt.tight_layout()
+        plt.savefig(plots_dir / 'sources_over_time.png', bbox_inches='tight')
+        plt.close()
+        print("Created sources over time chart: plots/sources_over_time.png")
+    except Exception as e:
+        print(f"Error creating time-based charts: {e}")
+
+# Content length distribution
+if 'content_length' in df.columns:
+    plt.figure(figsize=(12, 8))
+    sns.histplot(df['content_length'].clip(upper=5000), bins=50)
+    plt.title('Content Length Distribution (clipped at 5000 chars)', fontsize=14)
+    plt.xlabel('Content Length (characters)')
+    plt.ylabel('Count')
+    plt.tight_layout()
+    plt.savefig(plots_dir / 'content_length.png', bbox_inches='tight')
+    plt.close()
+    print("Created content length distribution: plots/content_length.png")
+
+print("\nAnalysis complete. Check the 'plots' directory for visualizations.")

metadata.json

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+{
+  "dataset_info": {
+    "description": "Bookmarks collected from various sources including Twitter, GitHub, and Raindrop.io",
+    "citation": "",
+    "homepage": "",
+    "license": "",
+    "features": {
+      "id": {
+        "dtype": "int64",
+        "description": "Unique identifier for the bookmark"
+      },
+      "source": {
+        "dtype": "string",
+        "description": "Source of the bookmark (twitter, github, raindrop, etc.)"
+      },
+      "title": {
+        "dtype": "string",
+        "description": "Title of the bookmark"
+      },
+      "url": {
+        "dtype": "string",
+        "description": "URL of the bookmark"
+      },
+      "content": {
+        "dtype": "string",
+        "description": "Content of the bookmark"
+      },
+      "created_at": {
+        "dtype": "string",
+        "description": "Creation date of the bookmark"
+      },
+      "domain": {
+        "dtype": "string",
+        "description": "Domain of the URL"
+      },
+      "content_length": {
+        "dtype": "int64",
+        "description": "Length of the content in characters"
+      },
+      "year": {
+        "dtype": "int64",
+        "description": "Year the bookmark was created"
+      },
+      "month": {
+        "dtype": "int64",
+        "description": "Month the bookmark was created"
+      },
+      "twitter_username": {
+        "dtype": "string",
+        "description": "Twitter username"
+      },
+      "twitter_name": {
+        "dtype": "string",
+        "description": "Twitter display name"
+      },
+      "twitter_followers": {
+        "dtype": "int64",
+        "description": "Number of Twitter followers"
+      },
+      "twitter_likes": {
+        "dtype": "int64",
+        "description": "Number of likes on the tweet"
+      },
+      "twitter_retweets": {
+        "dtype": "int64",
+        "description": "Number of retweets"
+      },
+      "twitter_replies": {
+        "dtype": "int64",
+        "description": "Number of replies to the tweet"
+      },
+      "github_repo": {
+        "dtype": "string",
+        "description": "GitHub repository name"
+      },
+      "github_stars": {
+        "dtype": "int64",
+        "description": "Number of stars on the GitHub repository"
+      },
+      "github_forks": {
+        "dtype": "int64",
+        "description": "Number of forks of the GitHub repository"
+      },
+      "github_owner": {
+        "dtype": "string",
+        "description": "Owner of the GitHub repository"
+      },
+      "github_language": {
+        "dtype": "string",
+        "description": "Primary language of the GitHub repository"
+      },
+      "raindrop_domain": {
+        "dtype": "string",
+        "description": "Domain saved in Raindrop.io"
+      },
+      "raindrop_tags": {
+        "dtype": "list",
+        "description": "Tags associated with the Raindrop bookmark",
+        "sequence": {
+          "dtype": "string"
+        }
+      }
+    }
+  }
+}