Build content aggregation systems with proxies. Covers multi-source collection, RSS-first strategy, deduplication, freshness monitoring, and legal frameworks.
What Content Aggregation Is and Why It Needs Proxies
Content aggregation is the practice of collecting, organizing, and presenting information from multiple sources into a unified feed. You interact with aggregators daily — Google News pulls from thousands of publishers, Kayak compiles flights from dozens of airlines, Zillow aggregates listings from multiple real estate databases, and Glassdoor combines reviews from companies across every industry.
The technical challenge is that each source is its own island. Different websites, different HTML structures, different anti-bot measures, different rate limits, different geographic restrictions. A news aggregator pulling from 500 publishers must handle 500 distinct scraping targets, each with its own quirks and defenses.
Content aggregation proxies solve the three core problems that make multi-source collection hard at scale:
Without proxies, an aggregation system scraping 100+ sources from a single IP would be blocked by most of them within hours.
The technical challenge is that each source is its own island. Different websites, different HTML structures, different anti-bot measures, different rate limits, different geographic restrictions. A news aggregator pulling from 500 publishers must handle 500 distinct scraping targets, each with its own quirks and defenses.
Content aggregation proxies solve the three core problems that make multi-source collection hard at scale:
- Rate limit distribution. Each source has its own request limits. A residential proxy pool lets you distribute requests across thousands of IPs, staying under every source's threshold simultaneously
- Geographic access. Content varies by country. A UK real estate aggregator needs UK-based IPs to see UK listings. A global news aggregator needs proxies in each target country to capture locally framed stories. Databay's coverage across 200 countries enables multi-region aggregation from a single infrastructure
- Anti-bot diversity. Each source deploys different anti-bot technology. Some use Cloudflare, others Akamai, others custom solutions. A diverse proxy pool with residential IPs provides the broad compatibility needed to access all sources reliably
Without proxies, an aggregation system scraping 100+ sources from a single IP would be blocked by most of them within hours.
RSS and API-First: The Smart Collection Strategy
Before writing a single line of scraping code for any source, check for RSS feeds and public APIs. This is not just good practice — it is the highest-leverage decision in aggregation system design.
RSS feeds are purpose-built for automated content consumption. They are structured, lightweight, and explicitly published for machine reading. A well-maintained RSS feed gives you headlines, summaries, publication dates, and content links in a clean XML format. Parsing RSS is trivial compared to scraping HTML, and RSS requests are rarely rate-limited or blocked because sites want feed readers to consume them.
The same logic applies to APIs. Many content platforms offer public or authenticated APIs that provide structured data: Reddit's API, Twitter/X's API, news wire APIs (AP, Reuters), government data APIs (data.gov, eurostat). API access is faster, more reliable, and more legally clear than scraping the same data from rendered web pages.
A practical content aggregation architecture prioritizes sources in this order:
In a 500-source news aggregator, you might collect 200 sources via RSS, 50 via API, and scrape the remaining 250. This tiered approach reduces proxy costs by 50% and parser maintenance by 60% compared to scraping everything.
RSS feeds are purpose-built for automated content consumption. They are structured, lightweight, and explicitly published for machine reading. A well-maintained RSS feed gives you headlines, summaries, publication dates, and content links in a clean XML format. Parsing RSS is trivial compared to scraping HTML, and RSS requests are rarely rate-limited or blocked because sites want feed readers to consume them.
The same logic applies to APIs. Many content platforms offer public or authenticated APIs that provide structured data: Reddit's API, Twitter/X's API, news wire APIs (AP, Reuters), government data APIs (data.gov, eurostat). API access is faster, more reliable, and more legally clear than scraping the same data from rendered web pages.
A practical content aggregation architecture prioritizes sources in this order:
- Tier 1: RSS feeds. Free, fast, structured, rarely blocked. Check every source domain for /feed, /rss, /atom paths. Parse the sitemap.xml for feed URLs. About 40-50% of news and blog sources offer RSS
- Tier 2: Public APIs. Structured data with documented rate limits. May require registration and API keys. Often free for moderate usage
- Tier 3: Web scraping with proxies. For sources without RSS or APIs. Requires proxy infrastructure, custom parsers, and ongoing maintenance as sites change
In a 500-source news aggregator, you might collect 200 sources via RSS, 50 via API, and scrape the remaining 250. This tiered approach reduces proxy costs by 50% and parser maintenance by 60% compared to scraping everything.
Building a Multi-Source Collection System
A production aggregation system needs architecture designed for source diversity. Each source has a different data format, update frequency, access method, and failure mode. The system must handle all of them through a unified pipeline.
Source-specific collectors. Each source gets its own collector module that handles the specifics of accessing that source — RSS parsing, API client, or web scraper with appropriate proxy configuration. Collectors are independent: one source's failure does not affect others. Each collector outputs a standardized intermediate format regardless of how it obtained the data.
Normalization layer. Raw collected content arrives in wildly different formats. A Reuters article has different fields than a Reddit post or a real estate listing. The normalization layer maps source-specific fields to a common schema: title, body, source, author, publication date, category, geographic relevance, and source URL. This layer also handles text encoding, date format standardization, and language detection.
Deduplication engine. The same story gets published by multiple outlets. The same product gets listed on multiple sites. The same job gets posted on multiple boards. Without deduplication, your aggregated feed is full of near-identical entries. This component detects and merges duplicates using techniques covered in a later section.
Categorization and tagging. Classify aggregated content by topic, relevance, and priority. Keyword-based rules handle simple cases. For sophisticated categorization, lightweight ML classifiers (trained on your historical data) assign topics and relevance scores. A well-categorized feed is orders of magnitude more useful than a chronological dump of everything collected.
Each component communicates through message queues, enabling independent scaling and fault isolation.
Source-specific collectors. Each source gets its own collector module that handles the specifics of accessing that source — RSS parsing, API client, or web scraper with appropriate proxy configuration. Collectors are independent: one source's failure does not affect others. Each collector outputs a standardized intermediate format regardless of how it obtained the data.
Normalization layer. Raw collected content arrives in wildly different formats. A Reuters article has different fields than a Reddit post or a real estate listing. The normalization layer maps source-specific fields to a common schema: title, body, source, author, publication date, category, geographic relevance, and source URL. This layer also handles text encoding, date format standardization, and language detection.
Deduplication engine. The same story gets published by multiple outlets. The same product gets listed on multiple sites. The same job gets posted on multiple boards. Without deduplication, your aggregated feed is full of near-identical entries. This component detects and merges duplicates using techniques covered in a later section.
Categorization and tagging. Classify aggregated content by topic, relevance, and priority. Keyword-based rules handle simple cases. For sophisticated categorization, lightweight ML classifiers (trained on your historical data) assign topics and relevance scores. A well-categorized feed is orders of magnitude more useful than a chronological dump of everything collected.
Each component communicates through message queues, enabling independent scaling and fault isolation.
Content Freshness: Knowing When to Check Each Source
Different content types change at fundamentally different frequencies. A news wire publishes new stories every few minutes. A price comparison site sees daily updates. A government database updates quarterly. Treating all sources equally wastes proxy bandwidth on static content and misses time-sensitive updates.
The freshness strategy should match the content type:
Adaptive freshness monitoring improves efficiency further. Track the actual change rate of each source by comparing consecutive scrapes. If a source that you check hourly has not changed in the last 10 checks, automatically reduce its check frequency. When it does change, gradually increase frequency again. This feedback loop optimizes proxy spend without manual tuning per source.
The freshness strategy should match the content type:
- Breaking news sources: Check every 2-5 minutes. Use RSS where available (most news sources provide it) to detect new articles with minimal bandwidth. Only scrape full articles when the RSS feed indicates new content
- Price and inventory data: Check every 1-4 hours during business hours, less frequently overnight. Price changes are time-sensitive for competitive intelligence but rarely happen minute-to-minute
- Job listings: Check every 6-12 hours. New postings accumulate throughout the day, but the competitive window for job aggregation is measured in hours, not minutes
- Real estate listings: Check every 12-24 hours. New listings appear daily, but the market moves slowly enough that daily collection captures meaningful changes
- Reference content: Check weekly or monthly. Company profiles, product specifications, and regulatory filings change infrequently
Adaptive freshness monitoring improves efficiency further. Track the actual change rate of each source by comparing consecutive scrapes. If a source that you check hourly has not changed in the last 10 checks, automatically reduce its check frequency. When it does change, gradually increase frequency again. This feedback loop optimizes proxy spend without manual tuning per source.
Handling Diverse Anti-Bot Across Sources
An aggregation system's biggest operational challenge is that each source uses different anti-bot technology. A strategy that works perfectly for one source fails completely on another. You need a flexible proxy and request strategy that adapts per source.
Profile each source by its protection level:
Unprotected sources (government databases, academic repositories, small blogs) need no special handling. Datacenter proxies work fine. Rate limiting and polite request intervals are sufficient. These sources account for 30-40% of a typical aggregator's targets and cost very little in proxy spend.
Lightly protected sources (mid-size news sites, standard e-commerce) use basic bot detection — User-Agent checks, simple rate limiting, maybe cookie verification. Datacenter proxies with proper headers and rotation handle these. Keep a rotating set of current User-Agent strings and maintain cookies across sessions.
Heavily protected sources (major platforms, large retailers, social media sites) deploy Cloudflare, Akamai, PerimeterX, or custom bot detection. These require residential proxies with browser-like fingerprints. For sources requiring JavaScript execution, headless browser instances through residential proxies are necessary. These sources are the most expensive to aggregate but often provide the most valuable content.
Build your collector framework to accept per-source configuration: proxy type, rotation strategy, request headers, rate limits, and whether to use a headless browser. Store these configurations in a database or configuration file, not hard-coded. When a source changes its anti-bot posture (upgrades from none to Cloudflare, for example), update the configuration without changing code.
Monitor success rates per source daily. A sudden drop indicates an anti-bot change that requires configuration adjustment.
Profile each source by its protection level:
Unprotected sources (government databases, academic repositories, small blogs) need no special handling. Datacenter proxies work fine. Rate limiting and polite request intervals are sufficient. These sources account for 30-40% of a typical aggregator's targets and cost very little in proxy spend.
Lightly protected sources (mid-size news sites, standard e-commerce) use basic bot detection — User-Agent checks, simple rate limiting, maybe cookie verification. Datacenter proxies with proper headers and rotation handle these. Keep a rotating set of current User-Agent strings and maintain cookies across sessions.
Heavily protected sources (major platforms, large retailers, social media sites) deploy Cloudflare, Akamai, PerimeterX, or custom bot detection. These require residential proxies with browser-like fingerprints. For sources requiring JavaScript execution, headless browser instances through residential proxies are necessary. These sources are the most expensive to aggregate but often provide the most valuable content.
Build your collector framework to accept per-source configuration: proxy type, rotation strategy, request headers, rate limits, and whether to use a headless browser. Store these configurations in a database or configuration file, not hard-coded. When a source changes its anti-bot posture (upgrades from none to Cloudflare, for example), update the configuration without changing code.
Monitor success rates per source daily. A sudden drop indicates an anti-bot change that requires configuration adjustment.
Content Deduplication Strategies
Deduplication is what separates a useful aggregated feed from a repetitive mess. The same news story published by 20 outlets should appear as one entry with multiple sources, not 20 near-identical entries. But deduplication in content aggregation is harder than exact matching — articles about the same event differ in wording, length, perspective, and detail.
Exact URL deduplication. The simplest layer — if you have already collected a URL, do not collect it again. Maintain a set of seen URLs and check before each request. This catches re-crawls of unchanged pages but misses the same content at different URLs (syndicated articles, reprinted press releases, cross-posted content).
Title similarity. Compare article titles using string similarity metrics (Jaccard similarity on word sets, or cosine similarity on TF-IDF vectors). Titles above 0.85 similarity for news articles about the same event are likely duplicates. This catches syndicated content with minor title variations but misses completely different titles for the same story.
Content fingerprinting. Generate MinHash or SimHash fingerprints of article body text. These locality-sensitive hashing techniques identify documents that share most of their content even with different introductions, conclusions, or editorial additions. A SimHash distance below a threshold (typically 3-5 bit differences for a 64-bit hash) indicates near-duplicates. This is the most effective technique for detecting syndicated content and lightly rewritten articles.
Semantic deduplication. For genuinely different articles covering the same event, use sentence embeddings (from models like all-MiniLM-L6-v2) to compute semantic similarity. Articles above 0.9 cosine similarity in embedding space are covering the same story even if they share minimal text. This is computationally expensive and best reserved for high-value aggregation feeds where quality justifies the cost.
Layer these techniques: URL dedup first (cheapest), then title similarity, then content fingerprinting, then semantic similarity only where needed.
Exact URL deduplication. The simplest layer — if you have already collected a URL, do not collect it again. Maintain a set of seen URLs and check before each request. This catches re-crawls of unchanged pages but misses the same content at different URLs (syndicated articles, reprinted press releases, cross-posted content).
Title similarity. Compare article titles using string similarity metrics (Jaccard similarity on word sets, or cosine similarity on TF-IDF vectors). Titles above 0.85 similarity for news articles about the same event are likely duplicates. This catches syndicated content with minor title variations but misses completely different titles for the same story.
Content fingerprinting. Generate MinHash or SimHash fingerprints of article body text. These locality-sensitive hashing techniques identify documents that share most of their content even with different introductions, conclusions, or editorial additions. A SimHash distance below a threshold (typically 3-5 bit differences for a 64-bit hash) indicates near-duplicates. This is the most effective technique for detecting syndicated content and lightly rewritten articles.
Semantic deduplication. For genuinely different articles covering the same event, use sentence embeddings (from models like all-MiniLM-L6-v2) to compute semantic similarity. Articles above 0.9 cosine similarity in embedding space are covering the same story even if they share minimal text. This is computationally expensive and best reserved for high-value aggregation feeds where quality justifies the cost.
Layer these techniques: URL dedup first (cheapest), then title similarity, then content fingerprinting, then semantic similarity only where needed.
Geographic Content Differences and Multi-Region Collection
The same website serves different content depending on where the request originates. A news outlet frames stories differently for its UK versus US audience. An e-commerce site shows different prices, products, and promotions by country. A job board lists geographically relevant positions. For content aggregation proxies to deliver complete coverage, you need multi-region collection.
The differences are not trivial. A study of major news websites showed that 35-45% of content was unique to specific geographic versions of the same site. Product prices vary by 10-30% across countries on the same retailer's website. Job listings are almost entirely geographic — a US-targeted search and a Germany-targeted search on the same platform return completely different results.
Multi-region collection architecture requires:
The differences are not trivial. A study of major news websites showed that 35-45% of content was unique to specific geographic versions of the same site. Product prices vary by 10-30% across countries on the same retailer's website. Job listings are almost entirely geographic — a US-targeted search and a Germany-targeted search on the same platform return completely different results.
Multi-region collection architecture requires:
- Geographic proxy routing. Route requests through proxies located in each target region. To collect UK content, use UK-based residential proxies. For German content, route through German IPs. Databay's country-level targeting makes this straightforward — specify the country in the proxy request and the infrastructure routes through the appropriate region
- Language-aware parsing. Different regions serve content in different languages. Your parsers need to handle multi-language extraction — date formats, number formats, text direction, and character encodings all vary by locale
- Region-tagged storage. Tag every collected item with its geographic origin so downstream consumers can filter by region. A global price comparison feed needs region tags to show users relevant pricing
- Cross-region deduplication. The same article published with minor regional adaptations should be identified as a single story with regional variants, not separate entries. Use content fingerprinting across regions to detect these near-duplicates
Building Alerting on Aggregated Data
Aggregated data becomes most valuable when it powers real-time alerting. Instead of users manually scanning a feed, automated alerts notify them when specific conditions are met. This transforms a passive data collection system into an active intelligence platform.
Keyword and entity alerts. Monitor incoming content for mentions of specific companies, products, people, or topics. A brand monitoring system might alert when a competitor launches a new product, when a company is mentioned in regulatory filings, or when a specific technology trend appears in industry publications. Implement this with full-text search indexing (Elasticsearch or PostgreSQL full-text search) that evaluates new content against stored alert rules in real time.
Anomaly detection alerts. Detect unusual patterns in aggregated data. A price monitoring aggregator should alert when a product's price deviates more than 15% from its trailing average. A news aggregator should alert when the volume of coverage about a topic spikes above normal levels — a surge in articles about a company often precedes significant news. A job listing aggregator should flag when a company's posting volume jumps, potentially indicating expansion or restructuring.
Competitive intelligence triggers. Set rules that fire when competitors take specific actions visible through aggregated public data: new product pages, pricing changes, job postings in new markets, press releases, patent filings, or regulatory submissions.
Sentiment shift detection. Track aggregated sentiment about entities over time using lightweight sentiment analysis models. Alert when sentiment shifts significantly — a brand's review sentiment dropping from 4.2 to 3.5 average over a week signals a product issue worth investigating.
Alert latency matters. For news and competitive intelligence, alerts should fire within minutes of content appearing. For price and listing monitoring, hourly is usually sufficient. Design your pipeline's freshness targets to match your alerting latency requirements.
Keyword and entity alerts. Monitor incoming content for mentions of specific companies, products, people, or topics. A brand monitoring system might alert when a competitor launches a new product, when a company is mentioned in regulatory filings, or when a specific technology trend appears in industry publications. Implement this with full-text search indexing (Elasticsearch or PostgreSQL full-text search) that evaluates new content against stored alert rules in real time.
Anomaly detection alerts. Detect unusual patterns in aggregated data. A price monitoring aggregator should alert when a product's price deviates more than 15% from its trailing average. A news aggregator should alert when the volume of coverage about a topic spikes above normal levels — a surge in articles about a company often precedes significant news. A job listing aggregator should flag when a company's posting volume jumps, potentially indicating expansion or restructuring.
Competitive intelligence triggers. Set rules that fire when competitors take specific actions visible through aggregated public data: new product pages, pricing changes, job postings in new markets, press releases, patent filings, or regulatory submissions.
Sentiment shift detection. Track aggregated sentiment about entities over time using lightweight sentiment analysis models. Alert when sentiment shifts significantly — a brand's review sentiment dropping from 4.2 to 3.5 average over a week signals a product issue worth investigating.
Alert latency matters. For news and competitive intelligence, alerts should fire within minutes of content appearing. For price and listing monitoring, hourly is usually sufficient. Design your pipeline's freshness targets to match your alerting latency requirements.
Legal Considerations for Content Aggregation
Content aggregation operates in a legally nuanced space. You are collecting and redistributing content created by others, which raises intellectual property questions that do not apply to data scraping for internal analytics.
Fair use and short excerpts. In the US, aggregating headlines and short summaries with links to original sources is generally protected under fair use — this is the model Google News established. Reproducing full articles is not fair use. The practical boundary: aggregate titles, publication dates, source attribution, and 1-2 sentence summaries. Always link to the original source. Never present aggregated content as your own.
The hot news doctrine. A legal concept primarily relevant in the US that protects the commercial value of time-sensitive factual reporting. If a news organization invests resources in breaking a story and your aggregator immediately republishes it, the original publisher may have a claim. This doctrine has narrow application but is worth understanding for real-time news aggregation.
EU Database Directive. In the European Union, databases created through substantial investment are protected even if the individual data points are factual and uncopyrightable. Systematically extracting a substantial part of a protected database can violate this right. This affects aggregators that comprehensively scrape product catalogs, listing databases, or directory services from EU-based sources.
Attribution requirements. Even when aggregation is legally permissible, attribution is both ethical and practically beneficial. Proper source attribution builds trust with users, creates goodwill with content creators, and provides a defense against claims of content misappropriation. Display the source name, publication date, and a direct link to the original for every aggregated item.
Consult a lawyer familiar with intellectual property and internet law before launching a commercial aggregation service. The legal landscape varies significantly by jurisdiction and content type.
Fair use and short excerpts. In the US, aggregating headlines and short summaries with links to original sources is generally protected under fair use — this is the model Google News established. Reproducing full articles is not fair use. The practical boundary: aggregate titles, publication dates, source attribution, and 1-2 sentence summaries. Always link to the original source. Never present aggregated content as your own.
The hot news doctrine. A legal concept primarily relevant in the US that protects the commercial value of time-sensitive factual reporting. If a news organization invests resources in breaking a story and your aggregator immediately republishes it, the original publisher may have a claim. This doctrine has narrow application but is worth understanding for real-time news aggregation.
EU Database Directive. In the European Union, databases created through substantial investment are protected even if the individual data points are factual and uncopyrightable. Systematically extracting a substantial part of a protected database can violate this right. This affects aggregators that comprehensively scrape product catalogs, listing databases, or directory services from EU-based sources.
Attribution requirements. Even when aggregation is legally permissible, attribution is both ethical and practically beneficial. Proper source attribution builds trust with users, creates goodwill with content creators, and provides a defense against claims of content misappropriation. Display the source name, publication date, and a direct link to the original for every aggregated item.
Consult a lawyer familiar with intellectual property and internet law before launching a commercial aggregation service. The legal landscape varies significantly by jurisdiction and content type.
Monetization Models for Aggregated Data
Content aggregation creates value by reducing information fragmentation. Users pay for the convenience of a single, organized view into data scattered across hundreds of sources. The monetization model should match the type of aggregation and the audience.
Subscription access. Charge users a recurring fee for access to the aggregated feed. This works best for professional-grade aggregation where the data has clear business value — competitive intelligence feeds, industry-specific news monitoring, real-time price tracking, and market research databases. Pricing is typically value-based: charge a fraction of what it would cost users to build and maintain the aggregation themselves.
Tiered data products. Offer a free or low-cost basic tier with limited sources, delayed data, or restricted features, and premium tiers with comprehensive coverage, real-time data, API access, and alerting. This lets users experience the value before committing to paid plans and segments the market by willingness to pay.
API licensing. Sell programmatic access to aggregated data for integration into customers' own systems. API pricing is typically per-request or per-record, scaling with usage. This model serves developers and data engineers who need raw data rather than a polished interface.
White-label aggregation. Build aggregation infrastructure that other companies rebrand and sell to their own customers. A real estate data aggregator might white-label to property management platforms. A price monitoring aggregator might white-label to e-commerce consultancies.
Regardless of model, track unit economics carefully. Your cost per aggregated data point (proxy costs + compute + storage + maintenance) must be significantly lower than the revenue per data point. Content aggregation proxies are typically the largest variable cost — optimize proxy spend by using the RSS-first strategy, adaptive freshness scheduling, and tiered proxy assignment described earlier.
Subscription access. Charge users a recurring fee for access to the aggregated feed. This works best for professional-grade aggregation where the data has clear business value — competitive intelligence feeds, industry-specific news monitoring, real-time price tracking, and market research databases. Pricing is typically value-based: charge a fraction of what it would cost users to build and maintain the aggregation themselves.
Tiered data products. Offer a free or low-cost basic tier with limited sources, delayed data, or restricted features, and premium tiers with comprehensive coverage, real-time data, API access, and alerting. This lets users experience the value before committing to paid plans and segments the market by willingness to pay.
API licensing. Sell programmatic access to aggregated data for integration into customers' own systems. API pricing is typically per-request or per-record, scaling with usage. This model serves developers and data engineers who need raw data rather than a polished interface.
White-label aggregation. Build aggregation infrastructure that other companies rebrand and sell to their own customers. A real estate data aggregator might white-label to property management platforms. A price monitoring aggregator might white-label to e-commerce consultancies.
Regardless of model, track unit economics carefully. Your cost per aggregated data point (proxy costs + compute + storage + maintenance) must be significantly lower than the revenue per data point. Content aggregation proxies are typically the largest variable cost — optimize proxy spend by using the RSS-first strategy, adaptive freshness scheduling, and tiered proxy assignment described earlier.
Frequently Asked Questions
What is content aggregation and how do proxies help?
Content aggregation is collecting and organizing information from multiple sources into a unified feed — news aggregators, price comparison sites, job boards, and review platforms are all examples. Proxies enable aggregation at scale by distributing requests across thousands of IP addresses to avoid rate limits on each source, providing geographic coverage to access region-specific content, and using residential IPs to reliably access sources with anti-bot protection.
Should I scrape content or use RSS feeds for aggregation?
Always check for RSS feeds and APIs before scraping. RSS is structured, lightweight, rarely blocked, and explicitly published for automated consumption. About 40-50% of news and blog sources offer RSS. Use a tiered approach: RSS first, public APIs second, web scraping with proxies only for sources without either. This reduces proxy costs by up to 50% and eliminates parser maintenance for RSS sources.
How do I detect duplicate content across multiple sources?
Layer four techniques in order of cost. First, URL deduplication to skip already-collected pages. Second, title similarity using Jaccard or cosine similarity to catch syndicated content with minor title variations. Third, content fingerprinting with MinHash or SimHash to detect near-duplicate articles sharing most of their text. Fourth, semantic embedding similarity for genuinely different articles covering the same event. Each layer catches duplicates the previous ones missed.
Is it legal to aggregate content from other websites?
Aggregating headlines, short summaries, and links to original sources is generally protected under fair use in the US, following the model established by Google News. Reproducing full articles is not fair use. In the EU, the Database Directive protects databases created through substantial investment. Always attribute sources, link to originals, and avoid reproducing substantial portions of copyrighted content. Consult a lawyer before launching a commercial aggregation service.
How often should I check each source in a content aggregation system?
Match check frequency to content type. Breaking news sources: every 2-5 minutes via RSS. Price and inventory data: every 1-4 hours during business hours. Job listings: every 6-12 hours. Real estate listings: every 12-24 hours. Reference content: weekly or monthly. Implement adaptive scheduling that reduces frequency for sources that rarely change and increases frequency for active sources. This optimizes proxy costs without missing updates.
