Large-Scale Reddit Data Collection for Boston University

Boston University is a leading private research institution with two primary campuses in Boston and academic programs operating around the world. Its research community spans 17 schools and colleges alongside an extensive network of laboratories, institutes, and specialized research centers. 

The university supports more than 37,000 students and has more than 4,300 faculty members. They needed access to large and diverse datasets for a study that examined public discourse, community behavior, social trends, and online interactions.

Reddit contains extensive public discussions across thousands of communities. This content can support research into social behavior, public opinion, emerging topics, and community activity, but gathering it at scale requires more than basic API requests. 

Researchers needed consistently structured data, covering posts, comments, and subreddit information. The collection process also had to operate within Reddit’s API limits while supporting recurring and historical research workloads.

Key Issues 

• Collecting historical and newly published content across multiple subreddits 
• Retrieving posts together with associated comment discussions 
• Distributing requests while staying within Reddit API limits 
• Maintaining consistent schemas across different Reddit record types 
• Preserving the latest state of records across recurring collection runs 
• Preparing large datasets for efficient analysis in BigQuery 
• Scaling collection beyond a single account or scraping instance 

A single collection process could not provide the required volume, coverage, or reliability. The project needed a distributed architecture that could gather data in parallel and preserve it in a query-able research environment.

A distributed data collection framework was developed to automate Reddit data acquisition, validation, and storage for large-scale research. The solution continuously gathered posts, comments, and subreddit metadata before transforming the data into analytics-ready datasets, which were stored in Google BigQuery. 

Distributed Data Collection 

Multiple Scrapy instances operated in parallel (using different Reddit accounts) to distribute API requests across the collection workload. This architecture increased collection capacity while respecting Reddit API rate limits and supporting large-scale subreddit coverage.  

Intelligent Data Processing 

Collected Reddit content was validated, transformed, and organized into separate processing workflows for posts, comments, and subreddit metadata. Structured schemas were used to ensure consistent data quality while simplifying downstream research and analytical queries.  

Cloud Data Warehouse 

Validated records were automatically loaded into Google BigQuery using an append-only architecture that preserved historical snapshots. Timestamp-based versioning enabled researchers to retrieve the latest state of each record while maintaining historical data for longitudinal analysis.  

Automated Research Infrastructure 

Google Cloud Scheduler orchestrated recurring collection jobs, while the cloud-native architecture supported scalable execution and efficient storage management. This provided a reliable foundation for trend research, sentiment analysis, business intelligence, and future machine learning initiatives.  

Key Deliverables 

• Developed a distributed Reddit data collection framework  
• Automated collection of posts, comments, and subreddit metadata  
• Implemented parallel collection across multiple Reddit accounts  
• Built structured data pipelines for Google BigQuery  
• Applied timestamp-based versioning for historical and latest-state analysis  
• Scheduled recurring collection workflows using Google Cloud Scheduler  
• Delivered analytics-ready datasets for research and business intelligence

Faster Research Data Availability 

Automated Reddit data collection replaced manual extraction, making fresh posts, comments, and subreddit information available for research with minimal operational effort.  

Improved Data Reliability 

Maintain consistent schemas and timestamp-based versioning across collected datasets, ensuring researchers always analyze reliable and up-to-date information.  

Cost-Optimized Analytics 

Reduced analytical query costs through partitioned BigQuery storage and selective collection strategies. This allowed the system to enable efficient processing of large datasets for research.  

Scalable Research Infrastructure 

Established a cloud-native collection framework capable of supporting parallel data acquisition, historical research, and future analytical workloads without increasing operational complexity.